Patient anatomy and task specific automatic exposure control in computed tomography

ABSTRACT

Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 17/471,420 filed on Sep. 10, 2021, entitled“PATIENT ANATOMY AND TASK SPECIFIC AUTOMATIC EXPOSURE CONTROL INCOMPUTED TOMOGRAPHY.” The entirety of the aforementioned application isincorporated by reference herein.

TECHNICAL FIELD

This application relates to medical image computed tomography (CT) scanoptimization and more particularly to techniques for tailoring automaticexposure control settings to specific patient anatomies and clinicaltasks.

BACKGROUND

Computed tomography (CT) has been one of the most successful imagingmodalities and has facilitated countless image-based medical proceduressince its invention decades ago. CT accounts for a large amount ofionizing radiation exposure, especially with the rapid growth in CTexaminations. Therefore, it is desirable to reduce the CT radiationdose. However, the reduction in dose also incurs additional noise andwith the degraded image quality, diagnostic performance can becompromised.

In this regard, radiation dose and image quality have traditionally beencompeting objectives in CT imaging. To balance the two, modern CTsystems use automatic exposure control (AEC), particularly tube currentmodulation (TCM) based on scout images. However, the goal ofconventional AEC algorithms is to provide a uniform noise level acrossthe entire imaged volume. While this is generally successful in avoidingexcessive radiation dose and maintaining image quality, it is largelybased solely on the general shape and size of the patient. In addition,conventional AEC algorithms use a metric for dose which reflects thetube output rather than actual dose to the patient. Conventional AECtechniques remain unaware of patient-specific anatomy that impacts organdose, do not account for image quality based on the task, and are proneto issues such as patient centering.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements or delineate any scope of thedifferent embodiments or any scope of the claims. Its sole purpose is topresent concepts in a simplified form as a prelude to the more detaileddescription that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products are provided that facilitate tailoringAEC settings to specific patient anatomies and clinical tasks usingmachine learning techniques.

According to an embodiment, a system is provided that comprises a memorythat stores computer executable components, and a processor thatexecutes the computer executable components stored in the memory. Thecomputer executable components comprise a reception component thatreceives one or more scout images captured of an anatomical region of apatient in association with performance of a CT scan of the anatomicalregion of the patient, wherein the anatomical region comprises one ormore organs. The computer executable components further comprise a doseestimation component that employs one or more first machine learningmodels to estimate, based on the one or more scout images, expectedorgan doses representative of expected radiation doses exposed to theone or more organs under different AEC patterns for the CT scan.

The one or more first machine learning models comprise a deep learningmodel trained using a supervised machine learning process and groundtruth data comprising a plurality of CT volumes generated from CT scansof the anatomical region under the different AEC patterns and organsegmentation dose maps generated from the CT volumes. In someimplementations, the computer executable components further comprise atraining component that trains the deep learning model using thesupervised machine learning process.

In some embodiments, the computer executable components further comprisea quality estimation component that employs one or more second machinelearning models to estimate, based on the one or more scout images,expected measures of image quality in a target region and a backgroundregion of scan images captured under the different AEC patterns. Thequality estimation component determines the target region and thebackground region based on a defined clinical task associated with theperformance of the CT scan, wherein the target region and the backgroundregion vary for different clinical tasks. The one or more second machinelearning models comprise a second learning model trained using asupervised machine learning process and ground truth data image qualitydata generated using a plurality of CT volumes generated from CT scansof the anatomical region under the different AEC patterns, wherein theground truth image quality data provides measures of image quality inthe target region and the background region as represented in the CTvolumes under the different AEC patterns. In some implementations, thecomputer executable components further comprise a training componentthat trains the second deep learning model using the supervised machinelearning process. The computer executable components can furthercomprise a simulation component that generates simulated noiseprojections using the CT volumes and generates the ground truth imagequality data based on the simulated noise projection.

The computer executable components can further comprise an optimizationcomponent that determines an optimal AEC pattern of the different AECpatterns for the performance of the CT scan based on the expected organdoses and the expected measures of image quality in a target region,wherein the optimal AEC pattern maximizes image quality in the targetregion and minimizes radiation doses to the organs. In this regard, thedifferent acquisition parameter values for one or more acquisitionparameters selected from the group consisting of: tube currentmodulation (TCM), tube voltage, collimation, filtration, bowtie, pitch,and start angle.

According to an embodiment, another system is provided that comprises amemory that stores computer executable components, and a processor thatexecutes the computer executable components stored in the memory. Thecomputer executable components comprise a training component that trainsa first deep learning network to determine expected organ dosesrepresentative of expected radiation doses exposed to one or more organsof an anatomical region under different AEC patterns of a CT scan basedon one or more scout images captured of the anatomical region fordifferent patients. The training component further trains a second deeplearning network to determine expected measures of image quality intarget regions and background regions of scan images captured under thedifferent AEC patterns based on the scout images. The computerexecutable components further comprise an optimization component thatemploys the first deep learning network and the second deep learningnetwork to determine optimal AEC patterns of the different AEC patternsfor the CT scan that maximize image quality in the target regions andminimize radiation doses to the one or more organs.

The target regions and the background regions vary for differentclinical tasks. In some implementations, the training component canfurther train a third deep learning network to determine the optimal AECpattern for each scout image and clinical task combination. The computerexecutable components can further comprise an inferencing component thatemploys the third deep learning network to determine the optimalautomatic exposure pattern for a new CT scan of the anatomical region ofa patient based on one or more new scout images captured of theanatomical region of the patient and a selected task of the differentclinical tasks.

In accordance with another embodiment, another system is provided thatcomprises a memory that stores computer executable components, and aprocessor that executes the computer executable components stored in thememory. The computer executable components comprise a receptioncomponent that receives task information identifying a clinical taskassociated with performance of a CT scan of an anatomical region of apatient, and one or more scout images captured of the anatomical region.The computer executable components further comprise an optimizationcomponent that determines an optimal AEC pattern for the performance ofthe CT scan based on the task information, the one or more scout imagesand using one or more machine learning techniques, wherein the optimalautomatic control pattern maximizes image quality in a target region ofscan images generated from the CT scan and minimizes radiation doses toone or more organs included in the anatomical region

The one or more machine learning techniques comprise employing a firstdeep learning network and a second deep learning network to determinethe optimal AEC pattern from amongst different candidate AEC patternsfor the CT scan that maximize the image quality in the target regionsand minimize radiation doses to the one or more organs. The first deeplearning network comprises one or more first machine learning modelsthat estimate, based on the one or more scout images, expected organdoses representative of expected radiation doses exposed to the one ormore organs under the different AEC patterns. The second deep learningnetwork comprises one or more second machine learning models thatestimate, based on the one or more scout images, expected measures ofimage quality in the target region and the background region of scanimages captured under the different AEC patterns.

In various implementations, in addition to determining the optimal AECpatten, the optimization component further determines an expected imagequality in the target region under the optimal AEC pattern and expectedradiation doses to the one or more organs under the optimal AEC pattern.In some implementations, the computer executable components furthercomprise an interface component that facilitates receiving user inputadjusting one or more parameters of the optimal exposure controlpattern, resulting in a modified AEC pattern, and wherein based onreception of the user input, the optimization component determines anupdated expected image quality in the target region and updated expectedradiation doses to the one or more organs under the modified AECpattern. The interface component can also facilitate receiving userinput identifying at least one of, a desired image quality for thetarget region and a desired radiation dose to the one or more organs,and wherein based on reception of the user input, the optimizationcomponent determines a modified AEC pattern that achieves the desiredimage quality and the desired radiation dose. The computer executablecomponents further comprise a control component operatively coupled toan imaging device that performs the CT scan and that controlsperformance of the CT scan by the imaging device based on the optimalautomatic exposure pattern.

In some embodiments, elements described in the disclosed systems andmethods can be embodied in different forms such as acomputer-implemented method, a computer program product, or anotherform.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of an example end-to-end process fortailoring the ACE setting of a CT scan based on the patient and clinicaltask in accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 2 presents a high-level flow diagram of an example machine learningprocess for generating patient and task customized AEC settings inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 3 presents an example computing system that facilitates tailoringAEC setting to specific patient anatomies and clinical tasks inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 4 presents an example dose estimation model in accordance with oneor more embodiments of the disclosed subject matter.

FIG. 5 presents an example dose estimation model architecture inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 6 illustrates an example pipeline for organ dose estimation from CTdata in accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 7 illustrates organ specific dose report information generated fromCT in accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 8 presents an example dose estimation component in accordance withone or more embodiments of the disclosed subject matter.

FIG. 9 illustrates dose estimation model training in accordance with oneor more embodiments of the disclosed subject matter.

FIG. 10 presents a high-level flow diagram of an example process forestimating organ doses under a selected AEC pattern based on one or morescout images in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 11 presents an example quality estimation model in accordance withone or more embodiments of the disclosed subject matter.

FIG. 12 illustrates quality estimation model training in accordance withone or more embodiments of the disclosed subject matter.

FIG. 13 illustrates an example quality estimation component inaccordance with one or more embodiments of the disclosed subject matter

FIG. 14 illustrates an example pipeline for organ noise estimation fromCT data in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 15 illustrates organ specific noise report information generatedfrom CT in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 16 illustrates example CT scan images generated from CT volume dataunder different radiation dosage percentages in accordance with one ormore embodiments of the disclosed subject matter.

FIG. 17 illustrates generation of a differential images from simulatednoise images in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 18 provides graphs illustrating measuring noise in uniform regionsacross simulated doses in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 19 presents a high-level flow diagram of an example process forestimating measures of image quality in target and background regionsunder a selected AEC pattern based on one or more scout images inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 20 illustrates an example optimization component in accordance withone or more embodiments of the disclosed subject matter.

FIG. 21 illustrates optimal AEC processing to determine an optimal AECbased on one or more scout images and a specific task in accordance withone or more embodiments of the disclosed subject matter.

FIG. 22 illustrates an example AEC optimization model in accordance withone or more embodiments of the disclosed subject matter.

FIG. 23 presents a high-level flow diagram of an examplecomputer-implemented process for determining an optimal AEC inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 24 presents a high-level flow diagram of an examplecomputer-implemented process for determining an optimal AEC inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 25 presents another example computing system that facilitatestailoring AEC setting to specific patient anatomies and clinical tasksin accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 26 presents a high-level flow diagram of an examplecomputer-implemented process for performing a CT exam using optimal AECin accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 27 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background section,Summary section or in the Detailed Description section.

The disclosed subject matter is directed to systems,computer-implemented methods, apparatus and/or computer program productsthat facilitate tailoring AEC settings to specific patient anatomies andclinical tasks. Conventional AEC techniques remain unaware ofpatient-specific anatomy that impacts organ dose, do not account forimage quality based on the task, and are prone to issues such as patientcentering. The proposed approach brings in an understanding of howdifferent AEC settings affect organ dose and organ noise to optimize thetube current profile and other AEC parameters. This is enabled usingdeep learning technology. Access to organ level metrics make thisapproach more task and patient specific and in addition providesadditional knobs for tube current optimization such as dose, advancedimage quality features.

The disclosed AEC optimization techniques aim to maximize the relevantimage quality for a fixed patient dose, or conversely, minimize dosewhile achieving the desired level of image quality. In going fromgeneric to highly customized imaging protocols, the optimal AEC patternfor a particular CT scan focuses image quality (and radiation) where itis most needed while avoiding radiation-sensitive organs. Thecustomization will be fully automated to integrate seamlessly with theworkflow. Such personalized imaging accounts for patient and technicalvariability, which in return reduces variability in image quality toproduce consistent, high-quality images.

Even with full volumetric information about the patient, this is not atrivial task, particularly if this is to be fast and automated. Currentmethods enable patient-specific organ dose calculation throughcomputationally intense dose maps like Monte Carlo simulation and withthe help of manual organ segmentation. They further require manual inputon localizing where image quality should be focused and where it is lessimportant. However, clinical workflow demands AEC be determined fromscout images, and in a fast and automated manner. Using new AI methodsand deep learning techniques, the disclosed AEC optimization strategyexploits the information in scout images to quickly and accuratelypredict patient-specific organ dose and image quality, thereby selectingthe optimal AEC for the clinical task.

To facilitate this end, the disclosed techniques train a first deeplearning model to directly infer organ doses from scout images and aprescribed AEC pattern. The training data uses matching CT volumes tocompute the Monte Carlo dose map from the scan and segments the organs(e.g., using one or more organ segmentation models) to provide organdose and total effective dose. The disclosed techniques further train asecond deep learning model to infer image quality from the scout imagesand an AEC pattern. The image quality reflects expected image quality indifferent anatomical regions, such that the optimal AEC pattern can betailored to optimize radiation and resulting image quality in a targetregion for the scan while minimizing radiation to the background region.The training data uses the same matching CT volumes to compute noise inprojections and the resulting reconstructed images. In variousembodiments, organ segmentation can also be used to map image qualitymetrics to the different organs, wherein the target regions and thebackground regions are based on the different organs.

The disclosed techniques further employ the first deep learning modeland the second deep learning model to determine an optimal AEC patternfor a particular patient and clinical task that delivers the requestedimage quality at the minimum dose. The disclosed techniques furtherbuild an end-to-end optimization model that maps a given scout image andtask to the optimal AEC in real-time. This optimization model can beintegrated into the imaging workflow to automatically determine theoptimal AEC for a CT scan in real-time based on the acquired scoutimages.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

FIG. 1 illustrates a diagram of an example end-to-end process 100 fortailoring the ACE setting of a CT scan based on the patient and clinicaltask in accordance with one or more embodiments of the disclosed subjectmatter. Process 100 is illustrated in accordance with performance of aCT scan of a patient 110 via an imaging system including a CT scanner108. The type of the CT scanner can vary. For example, The CT scanner108 can include a single energy scanner, a spectral CT scanner, andother types of CT scanners available in the industry.

In accordance with traditional CT imaging procedures, the patient isplaced on the scanner table and the anatomy of interest is positionedaround the center of the CT gantry. On or more scout images (e.g., scourimages 102) are then acquired, and the operator marks the scan range(e.g., start and end locations) over which the scan is to be acquired.The correct scan protocol is then selected by the operator based on theclinical indication for the examination (e.g., contrast vs.non-contrast), and then the specific scan parameters are selected toproduce scan images of the quality required for that diagnostic task.Finally, the scan images are captured and reconstructed using a range ofparameters that determine the characteristics of the images, such asimage sharpness and field of view (FOV).

With this context in mind, process 100 begins with the acquisition ofscout images 102 of the patient 110 in association with positioning ofthe patient 110 relative to the CT scanner 108 gantry. These scoutimages 102 can include one or more low-resolution images captured of thepatient's region of interest (ROI) to be scanned prior to capture of thesubsequent high-resolution (e.g., isotropic) three-dimensional (3D)image data of the ROI and the generation of the correspondinghigh-resolution reconstructed CT scan images. These scout images 102 aregenerally used to position/align the scanner relative to a desired scanprescription plane for which the subsequent 3D image data is captured.The scout images 102 are also used to define the 3D imaging coveragevolume or scan range for which the subsequent high-resolution scans arecaptured. In this regard, the scout images 102 can have a lowerresolution/image quality relative to the subsequently acquiredhigh-resolution scan images yet depict the same or wider region ofinterest. The terms “scout image,” “localizer image,” “calibrationimage,” “pilot image,” “reference image,” and the like are used hereininterchangeably unless context warrants particular distinction amongstthe terms. Each of these terms refers to a medical image captured of ananatomical region of a patient having a lower resolution relative to theCT scan images captured of the anatomical region of the patient. In theembodiment shown, the scout images 102 include two different imagescaptured from different anatomical orientations (e.g., lateral andfrontal) relative to patient and the 3D imaging space. However, thenumber and orientation of the scout images 102 can vary. For example, insome embodiments, a single scout image can be used, while in otherembodiments, two or more scout images can be used.

At 104, the scout images are processed using one or more AI models todetermine an optimal AEC pattern 106 for the patient 110 and a specificclinical task for which the CT scan is being performed. The optimal AEC106 defines parameters settings for the CT scan that control theradiation dose delivered to the patient and the resulting image qualityof the scan images 114. In CT scans, there is always a trade-off betweendose and image quality as images are acquired with the two competingobjectives of maximizing image quality and minimizing radiation dose.The AI model processing at 104 tailors these AEC parameter settings tothe patient 110 to account for the specific anatomy of the patient,which can vary based on patient size, shape, and the ROI being captured.The AI model processing at 104 also tailors these AEC setting to accountfor the specific region or regions of the resulting high resolution scanimages that are important for clinical evaluation (e.g., detecting liverlesions on a non-contrast scan), which can vary from patient to patientand the ROI being scanned. In particular, the optimal AEC pattern istailored to the patient anatomy to account for the estimated radiationdoses exposed to the organs of the patient 110 included in the ROI underdifferent AEC settings, as well as how these different AEC settingsimpact image quality in the specific region or regions of importance forclinical evaluation in the scan images. In this regard, the AI modelprocessing at 104 involves determining the optimal AEC settings thatresult in focusing radiation exposure in regions where it is most neededfor image quality and the clinical task, while avoiding or minimizingthe amount of radiation delivered to radiation-sensitive organs.

In the embodiment shown, the AI model processing at 104 involves organsegmentation 104 ₁, dose estimation 104 ₂, and image quality 104 ₃estimation. At a high level, the organ segmentation 104 ₁ involves usingmachine learning to automatically identify and segment the organsincluded in the scan ROI based on the scout images 102. The doseestimation 104 ₂ involves using machine learning to predict howdifferent AEC settings effect radiation doses to the organs. The imagequality 104 ₃ estimation involves using machine learning to predict howthese different AEC settings and radiation doses also affect imagequality in different regions of the scan images. The optimal AEC settingfor the patient is then determined based on the organ dose and imagequality estimations that minimizes radiation dose to the organs whilealso providing a desired level of image quality to the specific targetregion of importance for the clinical task. The optimal AEC settingevaluation and analysis can involve using one or more optimizationfunction as well as machine learning techniques. These machine learningand AI model processing techniques generally involve training one ormore deep learning models to perform the segmentation 104 ₁, the doseestimation 104 ₂, and the image quality 104 ₃ estimation prior to theirusage in the clinical workflow depicted in process 100. Additionaldetails regarding these machine learning and AI model processingtechniques are described in greater detail below with reference to FIGS.2-26 .

The specific parameters of the optimal AEC pattern that are determinedusing the AI model processing at 104 can vary. There are many aspects toAEC, including the selection of the tube voltage or the tube currentmodulation (TCM), collimation, filtration, bowtie, pitch and startangle. TCM refers to the modulation pattern of the tube current over thecourse of a CT scan. Conventional AEC algorithms use TCM to increase thetube current (i.e., the number of x-ray photons) for thicker bodyregions and decrease the tube current for thinner body regions. Aspatients are not homogeneous cylinders, the end result is typically thatthe tube current oscillates up and down within a single rotation of thegantry, and increases, on average, through thick body regions (e.g., theshoulders and hips), and decreases, on average, through thinner bodyregions (e.g., the chest). In this regard, the TCM can be adjusted todynamically control the number of photons at every projection throughthe patient.

The TCM significantly impacts radiation dose and corresponding imagequality and can vary rapidly across multiple CT acquisition rotations aswell as within a single rotation of the gantry. Thus, in variousembodiments, the optimal AEC pattern 106 defines the optimal TCMsettings for the CT scan that minimizes organ doses and maximizes imagequality in the target region. However, the disclosed techniques are notlimited to optimizing the TCM alone. In this regard, the optimal AEC 106can reflect different acquisition parameter values for variousadditional CT acquisition parameters, including but not limited to: tubevoltage (or tube potential), collimation, filtration, bowtie, pitch (orpitch factor), Z-axis coverage, source to detector distance, source torotation axis distance, start angle, angle between projections, verticaltranslation between projections, polar and azimuthal apertures, andvoxel spacings (isotropic). The start angle is of particular importanceto helical scans. For example, because helical scans have highlynon-uniform dose distributions, the start angle of helical CT scan beselected so that the beam avoids direct exposure of radiosensitiveorgans. The optimal AEC 106 can also account for different fluence fieldmodulation strategies that extend beyond the current hardwarecapabilities, such as a laterally shifting bowtie, or even a dynamicbowtie. In this regard, the term “AEC pattern” is used herein to referto the specific values of one or more CT scanner acquisition parametersthat can be adjusted per scan and/or over the course of the CT scan(e.g., TCM). The terms “AEC pattern,” “AEC settings,” “AEC parameters,”and the like are used herein interchangeably through the descriptionunless context warrants particular distinction amongst the terms.

Once the optimal AEC has been determined based on the scout images 102,the system (or operating technician) can configure the CT scanner toperform the CT scan using the parameter values/settings defined by theoptimal AEC. This configuration is performed prior to the performance ofthe actual CT scan (e.g., the actual acquisition of the 3D imaging CTdata) while the patient is positioned on the scanning table. In thisregard, in accordance with process 100, the optimal AEC can bedetermined and applied based on the scout images 102 in real-time at thestart of the imaging session. At 112, imaging device (e.g., the CTscanner 108) can perform the CT scan of the high-resolution imagecapture using the optimal AEC pattern. The resulting scan images 114will include optimal CT scan images for the patient and the clinicaltask. For example, the image quality (e.g., noise, resolution, etc.) ofthe scan images will be higher in the target region as opposed to thebackground region, wherein the target region represents the region ofclinical relevance for the purpose of the CT scan (e.g., the anatomicalregion being targeted for clinical evaluation).

FIG. 2 presents a high-level flow diagram of an example machine learningprocess 200 for generating patient and task customized AEC settings inaccordance with one or more embodiments of the disclosed subject matter.In various embodiments, one or more aspects of process 200 can beincorporated into the AI model processing 104 of process 100 todetermine the optimal AEC 106.

In accordance with process 200, at 202, a first deep learning network istrained to determine expected organ doses based on scout images underdifferent AEC patterns, wherein the scout images represent differentpatients, and wherein the expected organ doses reflect expectedradiation doses to organs included in the anatomical ROI represented inthe scout images. This first deep learning model is referred to hereinas the dose estimation model. As described in greater detail below withreference to FIGS. 4-11 , this training process uses matching CT volumescaptured for each of the patients and their scout images to compute theMonte Carlo dose map from the scan and segments the organs (e.g., usingone or more organ segmentation models) to provide organ dose and totaleffective dose. Once trained, the input to the dose estimation model caninclude one or more scout images, the scan range (or ROI) for the CTscan, and a selected AEC pattern. The output of the dose estimationmodel includes estimated radiation doses exposed to the organs in theROI under the selected AEC pattern. In some embodiments, the doseestimation model can be tailored to a specific anatomical ROI (e.g.,body part). With these embodiments, a plurality of different doseestimation models can be generated for different anatomical ROIs and/orCT scan types (e.g., abdomen, body, abdomen/pelvis, pelvis, CAP (chest,abdomen and pelvis), chest/abdomen, runoff, head, head/neck, C-spine,chest, extremity, TL-spine, L-spine, T-Spine, and facial bone). Atruntime, the system can select an apply the appropriate dose estimationmodel based on the ROI being scanned.

At 204, a second deep learning model is trained to determine based onthe scout images, expected measures of image quality in target regionsand background regions of high-resolution scan images captured under thedifferent AEC patterns for different tasks, wherein the target regionsand the background regions vary for different tasks. This second deeplearning model is referred to herein as a quality estimation model. Inone or more embodiments, the training data for the quality estimationmode uses the same matching CT volumes to compute noise in projectionsand the resulting reconstructed images under the different AEC patterns.The reconstructed images are further segmented to identify the targetregions and the backgrounds regions and the corresponding noise levelsare mapped thereto. In various embodiments, the target regions andbackground regions can be based on organ segmentation. With theseembodiments, organ segmentation can also be used to map the imagequality metrics (e.g., noise levels) to the different organs. Oncetrained, the input to the quality estimation model can include one ormore scout images, the scan range (or ROI) for the CT scan, the selectedAEC pattern, and the selected task (e.g., which indicates the desiredtarget region). The output of the quality estimation model includesestimated measures of image quality (e.g., measured as a function ofnoise levels and/or other quality evaluation metrics) in the target andbackground region under the selected AEC pattern. In implementations inwhich the target region corresponds to one or more organs, the measureof image quality can reflect image quality of the one or more organs inthe scan images. In some embodiments, the quality estimation model canalso be tailored to a specific anatomical ROI (e.g., body part). Withthese embodiments, a plurality of quality estimation models can begenerated for different anatomical ROIs and/or CT scan types (e.g.,abdomen, body, abdomen/pelvis, pelvis, CAP (chest, abdomen and pelvis),chest/abdomen, runoff, head, head/neck, C-spine, chest, extremity,TL-spine, L-spine, T-Spine, and facial bone). At runtime, the system canselect an apply the appropriate quality estimation model based on theROI being scanned.

Once the first (e.g., dose estimation) and second (e.g., qualityestimation) deep learning networks have been trained to a desired levelof performance, at 206 both networks can be employed to determineoptimal AEC patterns for different patients and tasks based on theirscout images. In this regard, based on the scout images, optimal AECpatterns for each of different clinical tasks (e.g., optimal ACE foreach scout image/task combination) are determined, wherein the optimalACE patterns maximize image quality in the target regions and minimizeradiation doses to the organs. In various embodiment, the optimizationanalysis performed at 206 models the AEC using one or more optimizationfunctions that balance the competing objectives of dose and imagequality using defined optimization criteria for the dose and imagequality metrics. For example, in some implementations, the optimizationfunction can be configured to find the optimal ACE that maximizes therelevant image quality for a fixed patient dose. In otherimplementations, the optimization function can be configured to find theoptimal ACE that minimizes dose while achieving a desired level of imagequality.

In some embodiments, the optimization analysis described above can beperformed at runtime (e.g., in the clinical workflow processes 100) todetermine the optimal AEC for a new patient based on their scout images.With these embodiments, the system will iteratively apply the doseestimation model and the quality estimation model to the scout imagesunder different AEC patterns to determine how different AEC patternoptions impact expected organ doses and image quality in the targetregion. The system can further automatically select the optimal ACEbased on model outputs using the optimization function and predefined oruser selected optimization criteria for the dose and image qualitymetrics.

Additionally, or alternatively, at 208 the system can train a third deeplearning network to determine the optimal AEC patterns for each of thetraining data scout image/task combinations evaluated at 206. In thisregard, the optimal AEC for each scout image/task combination determinedfor the training data at 206 can be used as ground truth to trainingdata to train the third deep learning network. Once trained, the inputto the third deep learning network will include one or more scout images(e.g., with the scan range and/or ROI indicated relative to the scoutimages) and the task. The third deep learning network will furtheroutput the optimal AEC. In some implementations, the third deep learningnetwork can also be configured to generate the expected organ doses andthe expected image quality metrics for the target region and thebackground region under the optimal AEC. At 210, this third deeplearning network can be employed in the clinical workflow to determinethe optimal AEC pattern for a new CT scan based on one or more new scoutimages and a selected task of the different clinical tasks 210. Withthese embodiments, the AI model processing at 104 in accordance withprocess 100 may involve application of only the third deep learningnetwork.

FIG. 3 presents an example computing system 300 that facilitatestailoring AEC setting to specific patient anatomies and clinical tasksin accordance with one or more embodiments of the disclosed subjectmatter. Embodiments of systems described herein can include one or moremachine-executable components embodied within one or more machines(e.g., embodied in one or more computer-readable storage mediaassociated with one or more machines). Such components, when executed bythe one or more machines (e.g., processors, computers, computingdevices, virtual machines, etc.) can cause the one or more machines toperform the operations described.

In this regard, computing system 300 provides an example computingsystem that includes machine-executable components configured to performone or more of the operations described in process 100, process 200 andadditional processes described herein. The computer executablecomponents include a dose estimation component 302, one or more doseestimation models 304, a quality estimation component 306, one or morequality estimation models 308, an optimization component 310, one ormore AEC optimization models 312, a reception component 314, a trainingcomponent 316 and an inferencing component 318. These computer/machineexecutable components (and other described herein) can be stored inmemory associated with the one or more machines. The memory can furtherbe operatively coupled to at least one processor, such that thecomponents can be executed by the at least one processor to perform theoperations described. For example, in some embodiments, thesecomputer/machine executable components can be stored in memory 324 ofthe computing system 300 which can be coupled to processing unit 322 forexecution thereof. The computing system 300 further include a system bus320 that communicatively and operatively couples the dose estimationcomponent 302, the one or more dose estimation models 304, the qualityestimation component 306, the one or more quality estimation models 308,the optimization component 310, the one or more AEC optimization models312, the reception component 314, the training component 316, theinferencing component 318, the processing unit 316 and the memory.Examples of said and memory and processor as well as other suitablecomputer or computing-based elements, can be found with reference toFIG. 27 , and can be used in connection with implementing one or more ofthe systems or components shown and described in connection with FIG. 3or other figures disclosed herein.

The deployment architecture of computing system 300 can vary. In someembodiments, the computing system 300 can correspond a single computingdevice (e.g., real or virtual). In other embodiments, the computingsystem 300 can correspond to two or more separate communicativelycoupled computing devices operating in a distributed computingenvironment. With these embodiments, one or more of the dose estimationcomponent 302, the one or more dose estimation models 304, the qualityestimation component 306, the one or more quality estimation models 308,the optimization component 310, the one or more AEC optimization models312, the reception component 314, the training component 316, theinferencing component 318 can be deployed at separate computing devices.The separate computing devices can be communicatively coupled via one ormore wired or wireless communication networks. In some implementations,the computing system 300 can include or be operatively coupled to themedical image scanning device (e.g., a CT machine such as the CT scanner108 or the like) that performs the CT scanning procedure in associationwith capture of the scout images and the high-resolution CT scan images.Additionally, or alternatively, the computing system 300 can include aseparate device/machine (e.g., real or virtual/cloud-based) that iscommunicatively coupled to the CT scanning device and/or an externalimage data storage system that provides the training data. Variousalternative deployment architecture variations can also be used.

With reference to FIGS. 1-3 , the dose estimation component 302 canperform the organ dose estimation operations described with reference toFIGS. 1 and 2 . The quality estimation component 306 can perform thetarget and background region scan image quality estimation operationsdescribed with reference to FIGS. 1 and 2 , and the optimizationcomponent 310 can perform the AEC optimization operations described withreference to FIGS. 1 and 2 .

As described with reference to FIG. 2 , the organ dose estimation, theimage quality estimation, and (optionally) the AEC optimization caninvolve training separate machine learning models to perform thecorresponding tasks. In the embodiment shown, these machine learningmodels respectively correspond to the one or more dose estimation models304, the one or more quality estimation models 308 and the one or moreAEC optimization models 312. To facilitate this end, the computingsystem 300 can include training component 310 that performs (orfacilitates performing) the machine learning model training anddevelopment using one or more supervised or semi-supervised machinelearning techniques. The computing system 300 can further include aninferencing component 312 that executes the trained models to generatetheir corresponding inference outputs. The computing system 300 furtherinclude a reception component 308 that receives training data (and/orthe information used to generate the training data) for training therespective models and the input data for model execution followingtraining. The features and functionalities of the dose estimationcomponent 302 and the one or more dose estimation models 304 aredescribed with reference to FIGS. 4-10 . The features andfunctionalities of the quality estimation component 306 and the one ormore quality estimation models 308 are described with reference to FIGS.11-19 , and the feature and functionalities of the optimizationcomponent 310 and the one or more AEC optimization models are describedwith reference to FIGS. 20-26 . Repetitive description of like elementsemployed in respective embodiments is omitted for sake of brevity.

Organ Dose Estimation

FIG. 4 presents an example dose estimation model 304 in accordance withone or more embodiments of the disclosed subject matter. As illustratedin FIG. 4 , the input to the does estimation model 304 can include oneor more scout images 402 and an AEC pattern. The one or more scoutimages 402 can include scout images captured of an anatomical region ofa patient in association with performance of a CT scan of the anatomicalregion. In some implementations, the scout images 402 can providedifferent perspectives of the anatomical region (e.g., axial, sagittal,coronal, etc.). In other implementations, a single scout image can beused. The AEC pattern 404 can define selected parameter values for oneor more AEC parameters in CT. In various embodiments, the AEC pattern404 can define a specific TCM scheme for the CT scan. Additionally, oralternatively, the AEC pattern 404 can define specific parameter valuesfor one or more other AEC parameters, including but not limited to: tubevoltage (or tube potential), collimation, filtration, bowtie, pitch (orpitch factor), Z-axis coverage, source to detector distance, source torotation axis distance, start angle, angle between projections, verticaltranslation between projections, polar and azimuthal apertures, andvoxel spacings (isotropic).

The output of the dose estimation model 304 includes organ doses 406that represent the estimated radiation doses absorbed by the organs ofthe patient in association with performance of the CT scan using the AECpattern 404. The dose estimation model 304 can also provide an estimateof the total effective dose under the AEC pattern. Effective dose iscalculated for the whole body. It is the addition of equivalent doses toall organs, each adjusted to account for the sensitivity of the organ toradiation. The effective dose is expressed in millisieverts (mSv). Forexample, the dose estimation model 304 can generate the effective dose(mSv) by combining the organ doses through a weighted sum of the organdoses using known International Commission on Radiological Protection(ICRP) tissue weighting factors.

The specific organs evaluated by the dose estimation mode are based onthe particular organs included in the ROI of the patient scanned. Insome implementations, the ROI can account for the entirety of theanatomical region represented in the scout images 402. In otherimplementations, the ROI can include a sub-region of the anatomicalregion depicted in the scout images. With these implementations,information defining the sub-region or scan range can also be providedas input with the scout images 402. In this regard, scout images areoften used to position the patient relative to the CT gantry and definethe scan range or portion of the body to be captured in the CT scan. Thescouts are almost always longer than the actual scans. Since our goal isto predict the dose in the actual scans, a scan range signal can beincluded with the input data to inform the dose estimation model 304about the focus region in the input scouts. This additional informationmakes the model informed about where to look at for dose estimation,given the input scout images. As described in greater detail infra, inestimating organ dose from the scout images 402, the dose estimationmodel 304 will implicitly learn organ segmentation as well to identifythe relevant organs.

In this example, the organs evaluated include lungs, liver, spleen,pancreas and kidneys. These organs are generally scanned in associationwith performance of a CT scan of the abdomen. Thus, in this example, theROI includes the patient's abdomen. However, the dose estimation model304 can be trained to evaluate any anatomical ROI and the relevantorgans associated therewith. The relevant organs for each anatomical ROIcan be predefined. As mentioned above, in some embodiments, differentdose estimation models 304 can be tailored to different anatomical ROIs.With these embodiments, during inferencing mode, the inferencingcomponent 318 can select and apply the appropriate dose estimation model304 for the particular ROI represented in the scout images.Additionally, or alternatively, the dose estimation model 304 caninclude a universal model configured evaluate any anatomical ROI. Withthese embodiments, the dose estimation model 304 can be trained toidentify the relevant organs in any ROI and tailor organ dose estimationfor those relevant organs.

The type or types of machine learning models used for the doseestimation model 304 can vary. In some embodiments, the dose estimationmodel 304 can include one or more deep learning models, such asconvolutional neural network (CNN)s, RESNET type models, and otherneural network models with different types of pooling techniques. Forexample, in one implementation, the dose estimation model 304 can employa CNN architecture, followed by a fully connected layer and regressionlayer that outputs the organ specific doses and the total effectivedose. Other suitable types of the machine learning models can includebut are not limited to, generative adversarial neural network models(GANs), long short-term memory models (LSTMs), attention-based models,transformers, decision tree-based models, Bayesian network models,regression models and the like. These models may be trained usingsupervised, unsupervised and/or semi-supervised machine learningtechniques.

FIG. 5 presents an example dose estimation model architecture that canbe used for the dose estimation model 304 in accordance with one or moreembodiments of the disclosed subject matter. In accordance with thisexample, the dose estimation model 304 comprises a CNN-based model. Inthis example, the dose estimation model 304 is used to predict the CTdose distribution at organs-of-interest from input frontal and lateralscout views of the patient's body within the indicated scan range underdifferent AEC settings. However, it should be appreciated that theanatomical ROI represented in the input scout images can vary and thenumber and type (e.g., orientation) of the input scout images can vary.

In the embodiment shown, the dose estimation model 304 comprises twomodules: a feature learning module (FLM) 502 and multiple dose modules(DM) 504 ₁-504 _(L+1), wherein L corresponds to the number of differentorgans evaluated. The FLM 502 is used to extract the shared features,later utilized through separate organ-specific DMs. In addition to organdose prediction, the model also estimates the patient's overall bodydose through a separate DM. Therefore, the dose outputs for the Ldifferent organs and patient (body) are generated via L+1 DMs (e.g.,DM₁, DM2, . . . , DM_(L+1)). All the predicted outputs are characterizedby mean doses.

In one example implementation, the FLM module can include eight 3×3convolutions with feature maps of 16, 16, 32, 32, 64, 128, 256, and 512respectively with stride 2 in the second and fourth convolutions. Eachof the convolutions can be followed by an instance normalization and aleaky ReLU activation with negative slope of 0.2. The feature maps canbe downsized by half after every two convolutions via 2×2 maxpooloperations. The CNN model can further include a global average poolinglayer and the features can be drawn across the channels. The extractedfeatures are then shared across the DMs. Each of DM modules may consistof two fully-connected (FC) layers (512 and 1.0 neurons respectively), aleaky ReLU activation with slope 0.2, and finally a sigmoid to outputthe mean dose prediction in the normalized scale.

In accordance with this example implementation, from the reference meandoses d₁ and the model predicted doses {circumflex over (d)}_(l) at anorgan labeled as l (l∈L), the loss for the dose estimation model 304 istherefore calculated in accordance with Equation 1 below, wherein whereM denotes the minibatch size, and the patient body dose is denoted at(L+1)-th.

$\begin{matrix}{{\mathcal{L}_{({d,\hat{d}})}^{dose} = {\frac{1}{M}{\sum\limits_{i}^{M}{\sum\limits_{l}^{L + 1}{{{d_{l}(i)} - {{\hat{d}}_{l}(i)}}}_{2}}}}},} & {{Equation}1.}\end{matrix}$

The reference organ dose values correspond to the ground truthinformation used to train the dose estimation model 304. In one or moreembodiments, the disclosed techniques generate these reference valuesfrom actual CT scan data.

In particular, the training data used to train and generate (e.g., testand validate) the dose estimation model 304 can include a plurality ofCT scans for different patients and their corresponding scout imagescaptured before the CT scans. In some implementations, the scout imagesfor the CT scans can include synthetically generated scout images. TheCT scans (and their paired scout images) can respectively depict thesame anatomical ROI in implementations in which the model is trained fora specific anatomical ROI (e.g., the abdomen). Additionally, oralternatively, the CT scans (and their paired scout images) can depict avariety of different anatomical ROIs. The CT scan data provides a 3Dvolume representation of the anatomical ROI.

In one or more embodiments, the GT (i.e. reference) organ dose valuescan be generated by processing the CT scan volumes are to compute dosemaps for the CT scan volumes under different AEC patterns. In variousembodiments, these dose maps can be generated using Monte Carlo (MC)simulations. The MC simulations can generate and track particles at thevoxel level and the deposited energy is calculated for patient-specificestimation of absorbed dose. An accelerated MC simulation has beenintroduced through graphics processing unit (GPU) computation, referredto as MC-GPU, which can accurately model the physics of x-ray photontransport in voxelized geometries. The disclosed techniques configurethis MC-GPU simulation method to account for different AEC patterns ofCT scanners (e.g., existing and future CT scanners) and employ it tocompute the dose maps for the CT volumes. In this regard, for a givenprotocol, the trajectory can be discretized into multiple sourcepositions per rotation. The source output is then modeled for eachposition, accounting for the AEC parameter values, including the tubevoltage, collimation, filtration, and bowtie. The dose map of eachsource position is further normalized per source photon, and then scaledaccording to the tube current and rotation time (mAs) represented bythat source position. This enables flexible adjustment of the tubecurrent modulation without having to re-run the MC simulation.

The organs are further segmented from these dose maps using one or moresegmentation models to provide the organ specific GT dose values and theGT total effective dose. In this regard, in estimating organ dose fromscout images, the organ dose model 304 will implicitly learn the organsegmentation. However, for training and testing purposes, the trainingcomponent 316 will need ground truth organ segmentations for each CTvolume, such as lung, heart, esophagus, stomach, intestines, liver,pancreas, spleen, kidneys, bladder, gonads, muscle, skin, and bone. Insome embodiments, the organ segmentation models can include separatemachine learning organ segmentation models (e.g., CNNs) that arespecific to a particular organ. With these embodiments, the specificorgan segmentation models that are applied can be based on the relevantorgans included in the ROI. In other embodiments, the organ segmentationmodels can include ROI specific organ segmentation model adapted tosegment all relevant organs included in a particular ROI. Still in otherembodiments, the organ segmentation models can include a universal organsegmentation model that is adapted to segment relevant organs in CT datafrom any ROI. The organ segmentation and dose maps are combined toestimate the organ doses (mGy), and the organ doses are combined intoeffective dose (mSv) through a weighted sum of organ doses, using ICRPtissue weighting factors. The corresponding scout images for the CTvolumes are then used to train the dose estimation model 304 to infersthese organ doses and the effective dose for the different AEC patterns.

FIG. 6 illustrates an example pipeline 600 for organ dose estimationfrom CT data in accordance with one or more embodiments of the disclosedsubject matter. Pipeline 600 starts with a CT scan data 602corresponding to a CT volume comprising a plurality of scan slices.Using an MC-GPU simulation method, the CT scan data 602 is used tocompute a voxel density map 604 which is then used to compute the bodydose map 606. The body dose map 606 is then segmented using an organsegmentation model to generate the corresponding organ dose map 608. Theorgan dose map 608 is further used to generate an organ dose report 610that provides the individual organ doses and the effective dose for aspecific AEC pattern.

In accordance with the embodiment shown in FIG. 6 , the input CT scandata 602 was interpolated and resampled to 128×128 in for each scanslice. The voxel density map 604 was created as voxelized patientphantom by mapping the CT Hounsfield Units (HU) to water-equivalentdensity, with isotropic voxels of 4×4×4 mm³ in accordance with Equation2.

$\begin{matrix}{V = {\frac{HU}{1000} + 1.}} & {{Equation}2}\end{matrix}$

The MC-GPU process was further modified to include the bowtie filtrationand anode heel effect. The anode heel effect leads to photon intensityvariation for azimuthal angles that can be modeled as a probabilityfunction. The bowtie model is based on basis material decomposition sothat the inputs for MC-GPU are the material attenuation coefficients andmaterial thickness combinations at different fan angles. With theseimplementations, once the source spectrum is input, the filtered spectraand relative photon intensity distribution for all fan angles can becomputed. Therefore, photon directions and energies can be sampledaccordingly.

The modified MC-GPU enables the analytical calculation of the groundtruth of the detected image given the bowtie model, the heel effectmodel, and the input spectrum. For individualized patient organ dose,the phantoms are generated from the patient CT voxel data. In thisregard, the input phantom data for MC simulation can contain the spatialmap of both material type and mass density. The density mapping isperformed following a piece-wise linear curve which defines thedensities of the mixture of water and bone.

In the embodiment shown in FIG. 6 , a helical scan was be simulated fromthe most superior to most inferior slice, with a pitch factor of 0.99,an 80 mm Z-axis coverage, 24 views per rotation, a 120 kVp spectrum, anda constant tube current. It should be appreciated however that these AECparameter values are merely exemplary and that the disclosed techniquescan be applied to generate organ dose maps for different TCM schemes andother AEC parameters. In the embodiment shown in FIG. 6 , the MC-GPUdose calculation was repeated with N=4 start angles (θ(i)) uniformlyspaced apart and averaged to obtain the dose map in accordance withEquation 3.

$\begin{matrix}{{D_{avg} = {\frac{1}{n}{\sum\limits_{i}^{N}{{{MCGPU}( {V,\psi} )}❘_{\theta(i)}}}}},} & {{Equation}3.}\end{matrix}$

In Equation 3, Ψ denotes the set of all the parameters used to configurethe MC-GPU simulation. The dose is reported as eV/g/photon and can bescaled to mGy (1 eV/g/photon=3.79 mGy/100 mAs) using a scanner-specificcalibration (e.g., CTDI measurement). The air was masked out the using apatient body mask with a threshold t_(air)=0.1 from the voxel geometry Vto obtain the body dose map 606. Therefore, the final dose map D can berepresented in accordance with Equation 4.

D=D _(avg)·(V>t _(air))   Equation 4.

The CT scan 602 was then segmented using a segmentation model thatleverages a 3D context encoder U-Net network. The context encoderutilized atrous convolution at different rates in the encoder networkwhich enables capturing longer range information compared to thestandard U-Net. The decoder of the segmentation model employed residualmulti-kernel pooling which performs max-pooling at multiple FOVs. Thesegmentation model was trained separately for the L different organs.The encoder and decoder networks utilized 5 convolution blocks followedby down-sampling and up-sampling respectively. The final convolution wasfollowed by a softmax activation. The segmentation model was trainedwith a focal categorical cross-entropy loss in accordance with Equation5, wherein i iterates over the number of channels and t iterates overthe number of voxels in the image patches.

L ( , ) seg = - ∑ t ∑ i α ⁡ ( 1 - t , i ) γ t , i log ⁢ ( t , i ) .Equation ⁢ 5

A weight map w_(t) was calculated to emphasize the voxels near theboundary of the reference in accordance with Equation 6, where d is theEuclidean distance from the closest label and wherein a and b are theweighting factors. Weighting factors a=2.0 and b=0.5 were chosen for theexperiments. The weight map was further added to the loss functionEquation 5.

w _(t) =a·exp(−b·d _(t))−1  Equation 6.

In order to calculate the organ-specific doses, the model predictedsegmentation mask (

) was interpolated and resampled to align the voxel coordinates to theCT voxel geometry prepared for the MC-GPU. The organ-specific dose mapO_(l), l∈L (e.g., organ dose map 608) was generated from the body dosemap D (e.g., body dose map 606) of Equation 4 and segmentation mask (

) in accordance with Equation 7.

O _(l) =D·

_(l); l∈L   Equation 7.

The organ-specific reference dose distributions were then determined andcharacterized by mean dose (d_(l)=mean(O_(l))) as well as mean body dose(d_(L+1)=mean(O_(L+1))).

FIG. 7 illustrates organ specific dose report information generated fromCT data using the techniques described above. In various embodiments,the organ dose report 610 can comprise the organ specific doseinformation illustrated in FIG. 7 for each of the respective segmentedorgans. In this example, the organ dose report data is demonstrated forthe body and six different organs associated with an abdomen scan,including the lungs, kidney, liver, bladder, spleen and pancreas. Theorgan dose report data plots the absorbed dose in mGy for differentvoxel positions and provides the mean dose across all voxel positions.

FIG. 8 presents an example dose estimation component 302 in accordancewith one or more embodiments of the disclosed subject matter. Repetitivedescription of like elements employed in respective embodiments isomitted for sake of brevity.

In some embodiments, the dose estimation component 302 can includetraining data generation component 802 to generate the ground truthorgan and effective body dose value information in accordance with theMC simulation and organ segmentation methods described above. In thisregard, the training data generation component 802 can receive pairedtraining scout images and CT volumes 801 for different patients. Thepaired training scout images and CT volumes 801 can depict the sameanatomical ROI in implementations in which the dose estimation model 304is trained for a particular anatomical ROI. Using the CT volumes, thetraining data generation component 802 can generate the ground truthorgan dose data 803 using the MC simulation and organ segmentationmethods described above. This GT organ dose data 803 can include, foreach of the training scout images (or groups of two or moreimplementations in which the input scout images include two or more,such as lateral and frontal for instance), organ doses and effectivedose for one or more AEC patterns. To facilitate this end, the trainingdata generation component 802 can include CT dose map generationcomponent 804, organ segmentation component 806 and one or more organsegmentation models 808.

In this regard, using the MC-GPU simulation method described above andthe CT volumes 801, the GT dose map generation component 804 can computedose maps for each of the CT scan volumes. The computed dose maps foreach CT volume can provide estimated 3D body dose maps (e.g., body dosemap 606) for the anatomical ROI under different AEC patterns. Forexample, in one or more embodiments, for a given protocol, the CT dosemap generation component 804 can discretize the photon trajectory intomultiple source positions per rotation. The CT dose map generationcomponent 804 can further model the source output for each position,accounting for the tube voltage, collimation, filtration, and bowtie.The CT dose map generation component 804 can further normalize the bodydose map of each source position per source photon. The CT dose mapgeneration component 804 can further scale the body dose map accordingto the tube current and rotation time (mAs) represented by that sourceposition. The CT dose map generation component 804 can further adjustthe TCM for different AEC patterns to generate the corresponding bodydose map values without having to re-run the Monte Carlo simulation.

The organ segmentation component 806 can then apply one or more organsegmentation models 808 to the body dose maps to segment the relevantorgans included in the ROI (e.g., which can be predefined) and generatethe organ specific doses for the different AEC patterns along with thetotal effective body dose for each CT scan volume. As described above,in some embodiments, the organ segmentation models 808 can include amulti-organ segmentation model adapted to segment all the relevantorgans for a specific anatomical ROI or any anatomical ROI. In otherimplementations, the organ segmentation models 808 can include separatemodels for each organ of interest.

The GT organ dose data 803 and the paired scout images can then be used(e.g., by the training component 316) to train the one or more doseestimation models 304 to infers these organ doses and the totaleffective dose as illustrated in FIG. 9 .

In this regard, FIG. 9 illustrates dose estimation model training inaccordance with one or more embodiments of the disclosed subject matter.As illustrated in FIG. 9 , the does estimation model training involvestraining the dose estimation model to predict the organ doses 904 (whichcan include the total effective dose) for a selected AEC pattern basedon one or more scout images using the ground truth CT generated organdoses described above. With reference to FIGS. 3 and 9 , in variousembodiments, the training component 316 can perform the dose estimationmodel training illustrated in FIG. 9 using one or more supervised orsemi-supervised machine learning training, testing and validationprocesses.

As illustrated in FIG. 9 , the training data 900 can include a pluralityof different training data sets, respectively identified as 902 _(1−N).The number of training data sets N can vary. Each training data setincludes three components respectively indicated with the letters A(component 1), B (component 2) and C (component 3). In particular, thefirst component A includes one or more scout images. In someimplementations, the scout images can also include or otherwise beassociated with information defining scan range. The second component Bincludes a known/defined AEC pattern, and the third component C includesthe GT organ doses (and effective dose) determined for that AEC pattern.As discussed above, these GT organ doses are generated using CT volumedata paired with the scout images A and the MC-simulated and organ dosemap generated for the AEC pattern. In this regard, with respect to oneexample training data set 902 ₁, the input to the dose estimation model304 includes the AEC pattern 901 _(1−B) and the one or more scout images901 _(1−A). The dose estimation model 304 is then trained to generatethe estimated organ doses 904 using the GT organ doses for the AECpattern 901 _(1−c).

It should be appreciated that the collective training data sets 902_(1−N) represent scout images and paired CT volumes for differentpatients and different AEC patterns. Additionally, the collectivetraining data sets 902 _(1−N) can include groups of training data setsthat include the same scout images (e.g., for the same patient), yetpaired with GT organ doses (and effective dose) for different AECpatterns generated from the same paired CT volume data. The collectivetraining data sets 902 _(1−N) will also depict the same anatomical ROIwhen the dose estimation model 304 is trained for a specific anatomicalROI (e.g., the abdomen for example).

In one or more embodiments, for organs directly exposed by the beam, thetarget accuracy of the dose estimation model can be set to be within 5%,as this is a typical error even for MC dose calculations. Organs notdirectly exposed, but receiving dose from scatter, may have higherpercent errors, though their absolute dose will be quite low. The totaleffective dose estimations can be set to be accurate within 1% since itaverages errors across all organs. To improve the network performanceand robustness, the training data 900 can also be augmented by randomlychanging the 3D position of the CT volumes (and generating correspondingscout images), as well as using different AEC patterns.

FIG. 10 presents a high-level flow diagram of an example process 1000for estimating organ doses under a selected AEC pattern based on one ormore scout images in accordance with one or more embodiments of thedisclosed subject matter. Process 1000 provides an example process thatcan be performed by computing system 300 using reception component 314,training component 316, dose estimation component 302 and inferencingcomponent 318. Repetitive description of like elements employed inrespective embodiments is omitted for sake of brevity.

In accordance with process 1000, at 1002, a system operatively coupledto a processor (e.g., system 300 or the like) can train (e.g., by thedose estimation component 302 using training component 316 in accordancewith FIG. 9 ), a deep learning network (e.g., a dose estimation model304), to determine expected organ doses representative of expectedradiation doses exposed to (or absorbed by) one or more organs of ananatomical region under different AEC patterns of a CT scan based on oneor more scout images captured of the anatomical region for differentpatients. As described above, the training component 316 can train thedose estimation model using GT organ doses determined for the differentpatient and AEC pattern combinations. In some embodiments, prior toperforming the training at 1002, process 1000 can also includegenerating the GT organ doses from matching CT volumes for the scoutimages using the MC simulated 3D dose maps under the different AECpatterns and subsequent organ segmentation of the 3D dose maps.

At 1004, once the deep learning network (i.e., the dose estimation model304) has been trained, tested and validated, the system can apply (e.g.,by the dose estimation component 302 using inferencing component 318)the deep learning network to one or more new scout images captured ofthe anatomical region of a patient to determine estimated organ doses tothe one or more organs under a selected AEC pattern.

Image Quality Estimation

The organ dose estimation techniques described above provide the toolsto quickly estimate organ doses for a given AEC pattern directly fromscout images. In this section, we evaluate the impact of different AECpatterns and/or their resulting organ doses on image quality. For agiven task, such as liver lesion detection on a contrast-enhanced scan,the disclosed techniques seek to maximize task-dependent image qualitysuch as contrast-to-noise ratio (CNR) while maintaining sufficient imagequality elsewhere. To facilitate this end, the disclosed techniquestrain a second deep learning network (e.g., one or more qualityestimation models 308) to predict image quality in both the target andbackground regions from a patient's scout image. These techniquesleverage the same training dataset of paired scout images and CT volumesdescribed above for organ dose estimation, using the CT volumes toestablish ground truth for image quality (IQ) in the target (IQ-target)and background (IQ-background) regions. Separately quantifying imagequality in these regions enables the CT scanner to be selectivelyconfigured with an AEC pattern that provides high quality images in thetarget region while providing a baseline image quality in the backgroundregion.

FIG. 11 presents an example quality estimation model 308 in accordancewith one or more embodiments of the disclosed subject matter. Repetitivedescription of like elements employed in respective embodiments isomitted for sake of brevity.

As illustrated in FIG. 11 , the input to the image quality estimationmodel 308 can include the one or more scout images 402, a selected AECpattern 404, and a selected task 1102. The task 1102 controls orotherwise defines the target and background region of the CT scanimages. In various embodiments, the task 1102 can include a clinicaltask for which the CT scan is performed. In this regard depending on theclinical indication of the CT example, the particular organs, anatomicalregions, etc., captured in the scan image that are of importance toclinical evaluation can vary. Additionally, or alternatively, the task1102 can include a particular post-processing task that will be used toprocess the CT scan images, wherein the post-processing task isfacilitated by higher levels of image quality in a target regionrelative to a background region. For example, in some implementations,the post-processing task involve processing the scan images using one ormore automated medical image inferencing models adapted to performautomated image processing tasks such as but not limited to:disease/condition classification, disease region segmentation, organsegmentation, disease quantification, disease/condition staging, riskprediction, temporal analysis, anomaly detection, anatomical featurecharacterization, medical image reconstruction, medical imageenhancement, and the like.

In one or more embodiments, the task 1102 can be selected from amongst adefine set of different tasks. In many example implementations, thetarget and background regions defined for the different tasks are basedon the organs included in the anatomical ROI. For example, for aparticular task, a target region could include one or more specificorgans (e.g., the liver in association with detecting liver lesions),while other organs and anatomical features included in the ROI could bedefined as background regions. However, the disclosed techniques are notlimited to defining target and background regions in the scan imagesbased on organs alone. In this regard, the target regions and backgroundregions can be based on other defined anatomical landmarks, specificscan lines, specific prescription planes, specific points/locations inthe scan images, and so on. The term target region is used herein torefer to the totality of the portion of the scan images considered atarget region. In this regard, the “target region” can include two ormore separate regions of the same CT scan image. Likewise, the“background region” can include two or more separate regions of the sameCT scan image.

The output of the quality estimation model 308 includes one or moreimage quality measure or metrics that represent measures of expectedimage quality in the target region (IQ-target) and the background region(IQ-background) of the CT scan images to be acquired under the selectedAEC pattern 404. In some embodiments, the measure of image quality canbe based on image noise levels determined by segmenting target andbackground regions and differentiating between morphological variationsand pixel differences due to photon noise. For example, in some exampleimplementations, as shown in FIG. 11 the expected image quality can beprovided a function of organ noise levels (measured in HU) and theoverall noise level (e.g., patient). Additionally, or alternatively, theimage quality measures can be based on one or more other factors,including but not limited to: mean noise, maximum noise, CNR,signal-to-noise ratio (SNR), noise power spectrum (NPS), detectivequantum efficiency (DQE), detectability index. In the example shown inFIG. 11 , the target regions and background regions are not explicitlyidentified but are understood to be based on the specific organs. Forexample, depending on the task 1102, the target region could include allof the organs or a subset (one or more) of the organs. Likewise, thebackground region could include a subset of the organs (e.g., one ormore). Still in another example, the patient column could represent thebackground region and correspond to the mean noise level of the regionof the scan images excluding the organs.

As with the organ dose estimation, in addition to the task 1102, thespecific target and background regions can vary based on the particularorgans included in the ROI of the patient scanned. In someimplementations, the ROI can account for the entirety of the anatomicalregion represented in the scout images 402. In other implementations,the ROI can include a sub-region of the anatomical region depicted inthe scout images. With these implementations, information defining thesub-region or scan range can also be provided as input with the scoutimages 402. As described in greater detail infra, in estimating targetand background region image quality from the scout images 402, thequality estimation model 308 will implicitly learn the relevant targetand background regions for each task and ROI, as well as how thedifferent AEC patterns affect image quality in the CT volumes. As withthe dose estimation models 304, in some embodiments, the qualityestimation model 308 can be tailored to a specific anatomical ROI. Withthese embodiments, different quality estimation models 308 can begenerated for different ROIs and selectively applied based on the ROIscanned.

The type or types of machine learning models used for the one or morequality estimation models 308 can vary. In some embodiments, the one ormore quality estimation models 308 can include one or more deep learningmodels, such as CNNs, RESNET type models, and other neural networkmodels with different types of pooling techniques. In someimplementations, the quality estimation model 308 can employ a CNNarchitecture corresponding to that illustrated in FIG. 5 . In anotherexample, the one or more dose estimation models can employ a CNNarchitecture that includes, followed by a fully connected layer andregression layer that outputs the estimated quality metrics for thetarget and background regions. Other suitable types of the machinelearning models can include but are not limited to, GAN, LSTMs,attention-based models, transformers, decision tree-based models,Bayesian network models, regression models and the like. These modelsmay be trained using supervised, unsupervised and/or semi-supervisedmachine learning techniques.

FIG. 12 illustrates quality estimation model training in accordance withone or more embodiments of the disclosed subject matter. As illustratedin FIG. 12 , the quality estimation model training involves training thedose estimation model to predict the one or more measure of imagequality in the target and background regions 1204 (e.g., IQ-target andIQ-background for a selected AEC pattern and task based on one or morescout images using ground truth CT generated target and background imagequality measures. Techniques for generating these ground truth imagequality metrics are discussed in greater detail below. With reference toFIGS. 3 and 12 , in various embodiments, the training component 316 canperform the quality estimation model training illustrated in FIG. 12using one or more supervised or semi-supervised machine learningtraining, testing and validation processes.

As illustrated in FIG. 12 , the training data 1200 can include aplurality of different training data sets, respectively identified as1202 _(1−N). The number of training data sets N can vary. Each trainingdata set includes four components respectively indicated with theletters A (component 1), B (component 2) and C (component 3) and D(component 4). In particular, the first component A includes one or moretraining scout images. These training scout images can include the samescout images used to train the dose estimation model 304 and/oradditional or alternative scout images. In some implementations, thescout images can also include or otherwise be associated withinformation defining scan range. The second component B includes aknown/defined AEC pattern. The known/defined AEC pattern can alsoinclude the different AEC patterns used to train the dose estimationmodel 304. The third component C includes the GT target and backgroundquality metrices determined for that AEC pattern. As discussed below,these GT quality metrics can also be generated using CT volume datapaired with the training scout images, which can include the same scoutimage-CT volume data pairs (e.g., paired training scout images and CTvolumes 801) used to generate the GT training data for the doseestimation model 304. The fourth component D includes a specific task,which controls/defines the relevant target and background regions.

In this regard, with respect to one example training data set 1202 ₁,the input to the quality estimation model 308 includes the task 1202_(1−D), the AEC pattern 1202 _(1−B) and the one or more training scoutimages 1201 _(1−A). The quality estimation model 1204 is then trained togenerate/estimate the target and background image quality metrics 1204for AEC pattern 1202 _(1−B) and task 1202 _(1−D) the based on thetraining scout images 1202 _(1−A) using the GT target and backgroundquality metrics 1202 _(1−C). The quality estimation model 308 willimplicitly learn the relevant task and background regions for the task,as well as how AEC affects image quality in the CT volumes. In variousimplementations, the training component 316 can train the qualityestimation model 308 to predict noise levels in these regions to within1.0 HU, which will provide the level of accuracy needed for optimizingAEC.

It should be appreciated that the collective training data sets 1202_(1−N) represent scout images and paired CT volumes for differentpatients and different AEC pattern and task combinations. In thisregard, the collective training data sets 1202 _(1−N) can include groupsof training data sets that include the same scout images for the samepatient and task, yet paired with GT target and background qualitymetrics for different AEC patterns generated from the same paired CTvolume data. Additionally, the collective training data sets 1202 _(1−N)can include groups of training data sets that include the same scoutimages for the same patient and AEC pattern, yet paired with GT targetand background quality metrics for different tasks generated from thesame paired CT volume data. The collective training data sets 1202_(1−N) will also depict the same anatomical ROI when the qualityestimation model 1204 is trained for a specific anatomical ROI (e.g.,the abdomen for example).

FIG. 13 presents an example quality dose estimation component 306 inaccordance with one or more embodiments of the disclosed subject matter.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

In some embodiments, the quality estimation component 306 can includetraining data generation component 1302 to generate the GT target andbackground quality data 1303 for training the one or more qualityestimation models 308. In this regard, the GT target and backgroundquality data 1303 can correspond to GT target and background qualitytraining data included in training data 1200 for different AEC and taskcombinations. As illustrated in FIG. 8 , in some embodiments, thetraining data generation component 1302 can employ the same pairedtraining scout images and CT volumes 801 used to generate the GT organdose data 803 to generate the GT target and background quality data1303. The paired training scout images and CT volumes 801 can depict thesame anatomical ROI in implementations in which the quality estimationmodel 308 is trained for a particular anatomical ROI.

In one or more embodiments, the training data generation component 1302can generate the GT target and background quality data 1303 using noisemaps generated from the CT volume data corresponding to the body dosemaps (e.g., body dose map 606). These noise maps can reflect differentnoise distributions in expected CT scan images generated under differentAEC patterns. These noise maps can further be segmented into target andbackground regions and the respective noise levels can be mapped to thecorresponding target and background regions, as illustrated in FIG. 14 .To facilitate this end, the training data generation component 1302 caninclude simulation component 1302, segmentation component 1306 and oneor more anatomy segmentation models 1308.

In this regard, FIG. 14 illustrates an example pipeline 1400 for organnoise estimation from CT data in accordance with one or more embodimentsof the disclosed subject matter. Pipeline 1400 starts with a CT scandata 1402 corresponding to one or more CT scan slices that make up a CTscan volume. With reference to FIGS. 13 and 14 , in one or moreembodiments, the simulation component 1302 can employ the CT scan data1402 to compute a body noise map 1404 that represents the distributionof noise in the CT scan data 1402 at a specific AEC pattern. Thesegmentation component 1306 can further segment the CT scan data 1402using one or more anatomy segmentation models 1308 to facilitateidentifying the target regions and background regions in the CT scandata 1402 as illustrated by the anatomy segmentation map 1406. Forexample, in various embodiments, the one or more anatomy segmentationmodels 1308 can include or correspond to the one or more organsegmentation models (e.g., organ segmentation models 808) used toperform the organ segmentation of the CT scan data for the organ dosemapping. With these embodiments, the segmented anatomy segmentation mapcan segment the organs anatomical features in the CT scan data 1402.Additionally, or alternatively, the one or more anatomy segmentationmodels 1406 can include segmentation models that are adapted to segmenta variety of different anatomical landmarks or features in the CT scandata 1402 (e.g., other than organs). For example, the other anatomicallandmarks or features can include bone, soft tissue, lesions, artifacts,scan planes, specific locations or points in the CT scan data 1402 andso on.

The training data generation component 1302 can further combine theanatomy segmentation map 1406 with the body noise map 1404 to obtain ananatomy specific noise map 1408. The anatomy specific noise map 1408provides a mapping of the body noise levels to the segmented anatomicalregions identified in the anatomy segmentation map. In this regard,using the body noise map and the anatomy segmentation map 1406, thetraining data generation component 1302 can determine the mean noiselevels associated with each of the segmented anatomical regions,including the specific landmarks and the background area exploiting thespecific landmarks. From this mapping, the training data generationcomponent 1302 can generate target and background noise report data 1410that identifies the mean noise level associated the target region andthe background region, wherein the mean noise levels reflect thespecific AEC pattern. Because the target and background regions can varybased on task, the target and background noise report data can also varyby task. For example, in some implementations in which the segmentedanatomical regions include organs in the ROI, the target and backgroundregion noise report data 1410 may identify the mean noise levels foreach of the segmented organs and the mean noise level for the region ofthe CT scan data excluding the organs. With these implementations, thetarget region may include all of the organs or a subset of the organs,and the background region may include the region excluding all or thesubset of the organs, depending on the task. In other embodiments, thetarget/background region noise report data 1410 can include the specificnoise levels for each anatomy segmented landmark/feature represented inthe anatomy segmentation map 1406 and the mean noise level for theremaining region (e.g., excluding the anatomy segmented regions). Fromthis data, the target and background regions can be selectively definedbased on task under the same AEC pattern. It should be appreciated thatthe same anatomy segmentation map 1406 can be mapped to different bodynoise maps generated from the same CT scan data 1402 for different AECpatterns. In this regard, as the body noise maps change depending on theAEC pattern modeled, the noise values for the selected target andbackground regions as defined based on the anatomy segmentation map 1406will also vary.

FIG. 15 illustrates example organ specific noise report informationgenerated from CT data using the techniques described above. The CTorgan noise report data illustrated in FIG. 15 provides example noisereport data that can be included in the target and background regionnoise report data 1410. In accordance with this example, organ anatomysegmentation was used to segment the relevant organs in the ROI usingthe same organ segmentation models 808 used to generate the organ dosereport data shown in FIG. 7 . In this regard, the mean noise levels werecalculated for each of the individual organs under a specific AECpattern. In this example, the CT organ noise dose report data isdemonstrated for the body (e.g., which can reflect the total imagequality and/or the region excluding the organs) and six different organsassociated with an abdomen scan, including the lungs, kidney, liver,bladder, spleen and pancreas. The organ dose report data plots the noiselevels (e.g., in HU) for different voxel positions and provides the meannoise level across all voxel positions for each component (e.g., theorgans and body).

With reference again to FIGS. 13 and 14 , the techniques employed by thesimulation component 1302 to generate the body noise maps 1404 fordifferent AEC patterns from the CT volume data can vary. In someembodiments, the simulation component 1302 can generate the body noisemaps 1404 from the CT volume data under different AEC patternsrepresenting different TCMs and/or other different AEC parameter valuesusing simulation techniques. In this regard, in association withperformance of a CT scan with TCM, as the tube current is modulated,image quality in the projections vary, which influence the image qualityin the reconstructed scan images. In some embodiments, to establish theGT target and background quality data 1303, the simulation component1302 can determine image quality metrics (e.g., noise and/or other imagequality metrics) for different AEC patterns by simulating projections atdifferent tube current levels (and thus different dosage level) usingthe CT volume and generating corresponding reconstructed scan images.For each projection, the simulation component 1302 can insert noisecorresponding to the tube current and spatial variation of the beam(e.g., due to bowtie), accounting for quantum and electronic noise, asillustrated in FIG. 16 . This requires an accurate model of the CTscanner, including its source spectrum and detector response.

In this regard, FIG. 16 illustrates example CT scan images generatedfrom CT volume data under different radiation dose percentages. Theupper set of scan images 1601 corresponds to scan images captured at100% dose, while the lower set of scan images 1602 illustrate scanimages generated at a 10% dose. In various embodiments, the simulationcomponent 1302 can generate different sets of reconstructed scan imagesfrom the raw CT volume data at different dose levels ranging from 100%to 10% by inserting noise into the raw CT data. In this example, theupper set of scan images 1601 corresponds to reconstructed scan imageswithout noise and the lower set of scan images 1602 corresponds toreconstructed scan images simulated without 90% noise. As can be seen bycomparison of the respective scan image sets, the high dose images 1601at 100% dose and no added noise have better visualization of tissues dueto lower noise relative to the low dose images 1602 with added noise.The scan images in both sets are respectively marked with boxes toindicate example target regions of interest.

In accordance with these embodiments, the simulation component 1302 cangenerate the reconstructed scan images from the simulated projectionswith and without added noise as illustrated in FIG. 16 . The simulationcomponent 1302 can generate multiple noise realizations at differentdose levels (e.g., 100% dose, 75% dose, 50% dose, 25% dose, 5% dose,etc.) to better estimate image noise levels. For filtered backprojection reconstructions, the noise should be highly realistic due toits quasi-linear nature. The simulation component 1302 can also employiterative reconstruction (e.g., ASIR) and deep learning imagereconstruction (DLIR) techniques to facilitate generating the simulateddose levels.

The simulation component 1302 can further determine the expected imagenoise distribution values for a scan image generated under a specificAEC pattern and/or at a specific dose level associated with the AECpattern based on the difference between the noise projections with andwithout added noise, as illustrated in FIG. 17 .

In this regard, FIG. 17 illustrates generation of differential imagesfrom a high dose image at 100% dose and a simulated low-dose image. Thefirst differential image 1701 depicts a differential noise imagegenerated based on the difference between the high dose image at 100%dose and another scan image simulated at 90% dose. The seconddifferential image 1702 depicts another differential noise imagegenerated based on the difference between the high dose image at 100%dose and another scan image simulated at 10% dose. The differentialimages show the added noise, but not the original noise at full dose.

In one or more embodiments, the simulation component 1302 can estimatenoise levels from the differential images (e.g., first differentialimage 1701, second differential image 1702, and the like), usingempirical curve fitting to estimate the standard deviation at anarbitrary dose. To facilitate this end, the simulation component 1302can first calculate standard deviation (stdev) maps of difference imagesat two simulated doses (which helps capture behavior across the doserange), in accordance with Equation 8.

σ_(Diff1)=the stdev map of difference images Im_(D1)−Im_(1.0); and

σ_(Diff2)=the stdev map of difference images Im_(D2)−Im_(1.0)  Equation8.

The simulation component 1302 can then estimate the standard deviationof the arbitrary dose D≤1 in accordance with Equation 9.

σ_(D)=σ_(Diff1)×(a ₁₁ /D+a ₁₂ /√{square root over (D)}+a₁₃)+σ_(Diff2)×(a ₂₁ /D+a ₂₂ /√{square root over (D)}+a ₂₃)    Equation9.

FIG. 18 provides graphs illustrating measured noise in uniform regionsacross all simulated doses in accordance with the techniques describedabove. Graph 1801 plots the noise levels (e.g., measured as a functionof the standard deviation of the difference images) of a plurality ofdifferent image regions as a function of dose, wherein each linecorresponds to a different region. Graph 1802 plots the normalized orarbitrary noise levels normalized to 100% dose.

FIG. 19 presents a high-level flow diagram of an example process 1900for estimating measures of image quality in target and backgroundregions under a selected AEC pattern based on one or more scout imagesin accordance with one or more embodiments of the disclosed subjectmatter. Process 1900 provides an example process that can be performedby computing system 300 using reception component 314, trainingcomponent 316, quality estimation component 306 and inferencingcomponent 318. Repetitive description of like elements employed inrespective embodiments is omitted for sake of brevity.

In accordance with process 1900, at 1902, a system operatively coupledto a processor (e.g., system 300 or the like) can train (e.g., by thequality estimation component 306 using training component 316 inaccordance with FIG. 12 ), a deep learning network (e.g., a qualityestimation model 306), to determine expected measure of image quality(e.g., noise levels and/or one or more other image quality metrics) intarget regions and background regions of scan images captured underdifferent AEC patterns based on scout images captured for differentpatients, wherein the target regions and the background regions fordifferent clinical tasks. In some embodiments, prior to performing thetraining at 1902, process 1900 can also include generating the GTtraining and background image quality metrics from matching CT volumesfor the scout images using simulated image quality maps under thedifferent AEC patterns and subsequent anatomy segmentation of the 3Ddose maps into the target and background regions for the differentclinical tasks.

At 1904, once the deep learning network (i.e., the quality estimationmodel 308) has been trained, tested and validated, the system can apply(e.g., by the quality estimation component 306 using inferencingcomponent 318) the deep learning network to one or more new scout imagescaptured of the anatomical region of a patient to determine estimatedmeasure of image quality in a target region and background region of CTscan images based on a selected AEC pattern and clinical task.

AEC Optimization

The previous sections provide deep learning techniques that enableseparately estimating patient specific organ doses under different AECsettings and estimating how the different organ doses and AEC settingsimpact expected image quality in the scan images for a specific task.Using these two separate deep learning networks (i.e., the doseestimation model 304 and the quality estimation model 308) the followingdescription leverages these two networks to facilitate determining theoptimal AEC settings for a particular patient and task while balancingthe competing objectives of minimizing radiation dose exposure to thepatient while achieving a desired level of image quality in the targetregion needed for clinical evaluation. To facilitate this end, thefollowing AEC optimization techniques employ the dose estimation model304 and the quality estimation model 308 to determine optimal AECsettings for different patients and different tasks based on theirtraining scout images that deliver the requested image quality at theminimum dose, formulating the optimal AEC as an optimization problem.These optimal AEC settings are then used as ground truth exemplars tobuild an end-to-end model that maps the scout and task to the optimalAEC in real-time.

With reference to FIG. 20 illustrated is an example optimizationcomponent 310 in accordance with one or more embodiments of thedisclosed subject matter. Repetitive description of like elementsemployed in respective embodiments is omitted for sake of brevity.

The optimization component 310 can include an optimal AEC evaluationcomponent 2002 and a deep learning optimization component 2008. Theoptimal AEC evaluation component 2002 can employ the trained doseestimation model(s) 304 and the trained quality estimation model(s) 308to determine and evaluate how different AEC settings affect organ doseand target and background region image quality directly from one or morescout images. The optimal AEC evaluation component 2002 can furtheremploy one or more combinatorial optimization techniques to iterativelyevaluate how different AEC patterns impact organ dose and image qualityin the target and background regions to converge on an optimal AEC for aspecific patient and task (wherein the task defines/controls the targetand background regions) that best achieves and/or balances (e.g., inaccordance with a defined weighting schemed) defined AEC optimizationcriteria 2004.

In various embodiments, the AEC optimization criteria 2004 can model AECas an optimization problem that balances the competing objectives ofdose minimization and quality maximization. In particular, the AECoptimization criteria 2004 can provide an optimization function thatdefines the optimal AEC a function of optimization criteria for theorgan doses and the image quality in the target region, wherein theoptimization criteria is based on minimizing organ dose and maximizingimage quality. For example, in some implementations, the optimizationfunction can be modeled as follows: given the scouts and a task,maximize image quality in the target region for a fixed dose subject tosystem constraints (i.e., the feasible space of AEC). In anotherimplementations, the optimization function can be modeled as follows:given the scouts and a task, minimize organ dose for a fixed imagequality in the target region, subject to system constraints (i.e., thefeasible space of AEC). Still in other embodiments, the optimizationcriteria of the optimization function can be based on defined thresholdsand/or ranges for maximum and minimum organ doses and target/backgroundregion image quality.

In some embodiments, the optimal AEC evaluation component 2002 canemploy a derivative-free optimization function such as covariance matrixadaptation evolution strategy (CMA-ES) to determine the optimal AEC fora specific scout image/task combination. Such optimization methodsemploy repeat evaluations of the objective function (dose and imagequality), as illustrated in FIG. 21 .

In this regard, FIG. 21 illustrates optimal AEC processing 2106 that canbe performed by the optimal AEC evaluation component 2002 to determinean optimal AEC 2110 based on one or more scout images 2102 and aspecific task 2104. The scout images 2102 can include one or more scoutimages (e.g., single perspective scout images, lateral and frontal scoutimages, etc.) captured of an anatomical ROI of a patient. For example,the scout images 2102 can correspond to scout images 102, scout images402, training scout images 902 _(1−A), training scout images 1202_(1−A), or the like. The task 2104 can include a selected task fromamongst the defined set of tasks for which the quality estimation model308 was trained.

In accordance with this embodiment, the optimal AEC processing 2106involve iteratively applying the dose estimation model 304 and thequality estimation model 308 to the scout images under differentcandidate AEC patterns 2108 and determining the expected organ doses andtarget and background image quality metrics under each of the candidateAEC patterns 2108. While each evaluation of the deep learning models isexpected to be sub-second, the overall optimization processing time mayrange from second to several minutes depending on the processing speedof the processing hardware used for the many evaluations of theobjective function. The optimal AEC evaluation component 2002 thenselects the optimal AEC 2110 from amongst the candidate AEC patternsthat best achieves the optimization criteria for the organ doses andtarget/background region image quality. In this regard, assuming theoptimization criteria is based on maximum fixed dose, the optimal AECevaluation component 2002 can find the best AEC pattern that achievesthe highest image quality in the target region at the maximum fixeddose. In other implementation, the optimization criteria can be tailoredto balance dose and target region image quality based on definedweightings for the organ dose and the target region image quality. Withthese implementations, the optimal AEC 2110 may include an AEC thatprovides a high level of image quality in the target region (e.g., butnot the highest), while also providing low organ doses (e.g., but notthe lowest).

In some embodiments, the optimal AEC processing 2106 illustrated in FIG.21 can be used in association with performance of an actual CT scanprior to the capture of the high-resolution scan images (e.g., while thepatient is on the scanning table). For example, with reference again toFIG. 1 , the optimal AEC processing 2106 can correspond to the AI modelprocessing at 104. With these implementations, the optimal AECprocessing 2106 can be used to determine the optimal AEC for the patientand task for an actual imaging procedure in the clinical workflow.

In other embodiments, the scout images 2102 can correspond to trainingscout images included in a set of training scout images. For example,the set of training scout images can include the same training scoutimages used to train the dose estimation model 304 and/or the qualityestimation model (e.g., the training scout images included in trainingdata 900 and/or training data 1200). In other implementations, thetraining scout images used as input for the optimal AEC processing 2106can include new scout images. Either way, with these embodiments, theoptimal AEC processing 2106 can be applied to each of the training scoutimages to determine optimal AEC patterns for each training scout imageand task combination. These optimal AEC patterns can then be used as theGT exemplars to train and develop an end-to-end model that takes scoutsand a task as input and directly outputs the optimal AEC.

With reference again to FIG. 20 , in the embodiment shown, thisend-to-end model can include one or more AEC optimization models 312.With these embodiments, the optimal AEC data 2006 can correspond to theGT optimal AEC patterns for the different scout image/task combinations.In this regard, the deep learning optimization component 2008 can employthe training component 316 to train one or more AEC optimization models312 to take one or more scout images and selected task as input anddirectly output the optimal AEC. The training data will consist of thepredetermined optimal AECs for different scout image/task combinationsusing the optimal AEC evaluation component 2002 and the techniquesdescribed with reference to FIG. 21 . In this regard, while optimal AECevaluation component 2002 explicitly estimates dose and image qualityfor each AEC candidate, the AEC optimization model 312 can be trained toimplicitly model these in its prediction of the optimal AEC. This directprediction should run in real-time in inference mode.

The type or types of machine learning models used for the one or moreAEC optimization model 312 can vary. In some embodiments, the one ormore AEC optimization model 312 can vary can include one or more deeplearning models, such as CNNs, RESNET type models, and other neuralnetwork models with different types of pooling techniques. Othersuitable types of the machine learning models can include but are notlimited to, GAN, LSTMs, attention-based models, transformers, decisiontree-based models, Bayesian network models, regression models and thelike. These models may be trained using supervised, unsupervised and/orsemi-supervised machine learning techniques. The number of AECoptimization models 312 can also vary. In this regard, in someembodiment, a plurality of different AEC optimization models 312 may betrained and tailored to different anatomical ROIs synonymous with thecorresponding dose estimation models 304 and quality estimation models308. In other embodiments, universal AEC optimization model 312 can bedeveloped that can be applied to any anatomical ROI.

FIG. 22 illustrates an example AEC optimization model 312 once trainedin accordance with one or more embodiments. In various embodiments, thelearning optimization component 2008 (and/or the inferencing component318) can apply the AEC optimization model 312 in the clinical workflowto provide real-time AEC optimization for CT imaging scans. For example,with reference again to FIG. 1 , the AI model processing at 104 cancorrespond to application of the AEC optimization model 312 asillustrated in FIG. 22 . In this regard, once trained, the input to theAEC optimization model 312 can include a one or more scout images 2202captured of an anatomical region of a patient and a selected task 2204.The output of the AEC optimization model 312 includes the optimal AEC2206.

In some embodiments, the AEC optimization model 312 can also beconfigured to generate the expected organ doses 2208 and the expectedtarget and background image quality 2210 for the optimal AEC pattern2206. Additionally, or alternatively, once the optimal AEC 2206 has beengenerated by the AEC optimization model 312, the dose estimation model304 and/or the quality estimation model 308 can then be separatelyapplied to the scout images 2202 using the optimal AEC 2206 as theselected AEC pattern to determine the organ doses 2208 and the expectedtarget and background image quality 2210 for the optimal AEC pattern2206. Therefore, as the user selects the task or modifies otheracquisition parameters, the resulting impact on dose and IQ willdynamically be made available, as well as an optimized AEC ready to scanthe patient. The delivered AEC and predicted dose and IQ can be includedin the scan report.

FIG. 23 presents a high-level flow diagram of an examplecomputer-implemented process 2300 for determining an optimal AEC inaccordance with one or more embodiments of the disclosed subject matter.Process 2300 provides an example process that for performance by theoptimal AEC evaluation component 2002 in accordance with variousembodiments described herein. Repetitive description of like elementsemployed in respective embodiments is omitted for sake of brevity.

In accordance with process 2300, at 2302, a system, operatively coupledto a processor (e.g., system 300 and the like), receives (e.g., viareception component 314) task information identifying a task associatedwith performance of a CT scan of an anatomical region of a patient, andone or more scout images captured of the anatomical region. For example,in some implementations, the scout images and task information can bereceived in association with performance of an actual CT scan prior tothe capture of the high-resolution scan images (e.g., while the patientis on the scanning table). With these implementations, process 2300 canbe used to determine the optimal AEC for the patient and task for anactual imaging procedure in the clinical workflow. In otherimplementations, the scout images can correspond to training scoutimages included in a set of training scout images. With theseimplementations, process 2300 can be applied to each of the trainingscout images to determine optimal AEC patterns for each training andtask combination. These optimal AEC patterns can be used by the deeplearning optimization component 2008 (and the training component 316) totrain and develop the one or more AEC optimization models 312.

At 2304, the system employs a first deep learning network (e.g., doseestimation model 304) to determine based on the one or more scout images(e.g., using optimal AEC evaluation component 2002), expected organdoses representative of expected radiation doses exposed to (or absorbedby) one or more organs in the anatomical region under different AECpatterns for the CT scan. At 2306, the system employs a second deeplearning network (e.g., quality estimation model 308) to determine,based on the one or more scout images (e.g., using optimal AECevaluation component 2002), expected measures of image quality in targetand background regions of scan images captured under the different AECpatterns, wherein the target and background regions are based on thetask. At 2308, the system determines (e.g., using optimal AEC evaluationcomponent 2002), based on the expected organ doses and the expectedmeasures of image quality under the different AEC patterns, an optimalAEC pattern of the different AEC patterns that maximizes image qualityin the target region and minimizes the radiation doses to the one ormore organs using an optimization function (e.g., provided by the AECoptimization criteria 2004).

FIG. 24 presents a high-level flow diagram of another examplecomputer-implemented process 2400 for determining an optimal AEC inaccordance with one or more embodiments of the disclosed subject matter.Process 2400 provides an example process for performance by the optimalAEC evaluation component 2002 and the deep learning optimizationcomponent 2008 in accordance with various embodiments described herein.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

In accordance with process 2400, at 2402, a system, operatively coupledto a processor (e.g., system 300 and the like), determines (e.g., usingoptimal AEC evaluation component 2002), optimal AEC patterns fordifferent scout image and task combinations based on expected organdoses and expected measure of target and background region image qualityunder different AEC patterns, wherein the determining comprises using anobjective function that maximizes image quality in the targe regions andminimizes the organ doses. At 2404, the system trains a deep learningnetwork (e.g., an AEC optimization model 312) to determine the optimalAEC patterns for the different scour image and task combinations (e.g.,via the deep learning optimization component 2008 and the trainingcomponent 316). At 2406, the system employs the deep learning network todetermine an optimal AEC pattern for a new CT scan based on a new scoutimage (or images) and a selected task of the different tasks (e.g., viathe deep learning optimization component 2008 and the inferencingcomponent 318).

FIG. 25 presents another example computing system 2500 that facilitatestailoring AEC setting to specific patient anatomies and clinical tasksin accordance with one or more embodiments of the disclosed subjectmatter. Computing system 2500 includes a computing device 2502 and a CTscanner 108. The computing device 2502 can be operatively and/orcommunicatively coupled to the CT scanner 108 and provide forcontrolling one or more operations of the CT scanner 108. The computingdevice 2502 can include same or similar components as computing system300 with the addition of interface component 2504, parameter adjustmentcomponent 2506, acquisition control component 2508 and display 2506.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

In accordance with system 2500, the training component 316 has beexcluded from the computing device 2502 to indicate an implementation inwhich the training and development of the one or more dose estimationmodels 304, the one or more quality estimation models 208 and the one ormore AEC optimization models 312 has be completed. In this regard,system 2500 provides an example run-time environment wherein one or moreof these models can be executed in real-time by the inferencingcomponent 318 in association performing an actual CT scan on a patient110 in accordance with process 100. With these embodiments, thecomputing device 2502 can correspond to a computing device employed bythe CT scanner operating technician (or another suitable entity).

For example, prior to the performance of the CT scan and the acquisitionof high-resolution CT scan images, the reception component 314 canreceive one or more low resolution scout images of the anatomical ROI ofthe patient to be scanned. The reception component 314 can also receiveinformation selecting/defining the relevant task for the performance ofthe CT scan, which controls/defines the relevant target and backgroundregions for the high-resolution scan images. The inferencing component318 can further apply the one or more AEC optimization models 312 to thescout images under the selected task to generate an optimal AEC pattern(e.g., optimal AEC settings) for the patient and task. In someembodiments, the inferencing component 318 can also apply the one ormore dose estimation models 304 to the scout images under the optimalAEC pattern to generate the estimated organ doses that will be absorbedunder the optimal AEC pattern. The inferencing component 318 can alsoapply the one or more quality estimation models 308 to the scout imagesunder the optimal AEC pattern and task to generate the estimated measureof target and background region image quality. Information identifyingthe optimal AEC pattern, the estimated organ doses and/or the measure oftarget and background region image quality can be presented to theoperating technician via the display. The acquisition control component2508 can further automatically configure the CT scanner to perform theCT scan (e.g., the high-resolution CT scanning process) using theoptimal AEC pattern.

To facilitate this end, the interface component 2504 can provide aninteractive graphical user interface (GUI) that can be presented to theoperating technician via the display 2506 in association with performingAEC and configuring and controlling the CT acquisition by the CTscanner. The interactive GUI can facilitates receiving user inputselecting/defining the task (and/or adjusting AEC optimizationcriteria), executing the one or more models, presenting the modeloutputs, and controlling one or more operations of the CT scanner 108.For example, the GUI can provide controls for receiving user inputidentifying one or more parameters of the CT acquisitions, receiving thescout images and defining/selecting the task for the CT exam. In someembodiments, the GUI can include an optimal AEC control thatautomatically generates the optimal AEC for the patient and task basedon the scout images using the technique described herein. Theacquisition control component 2508 can further configure the CT scannerto perform the CT exam using the optimal AEC pattern eitherautomatically and/or in response to user input provided by the operatingtechnician requesting usage of the optimal AEC pattern by the CT scanner108. For example, the acquisition control component can be operativelycoupled to the imaging device that performs the CT scan (e.g., the CTscanner 108) and control performance of the CT scan by the imagingdevice based on the optimal AEC pattern.

The parameter adjustment component 2506 can also provide additionaltools (e.g., accessed via the GUI) that allow the operator to provideinput adjusting one or more parameters of the optimal AEC and/orproviding optimization criteria for the optimal AEC (e.g., a desiredimage quality level and/or a desired organ dose distribution). Forexample, the parameter adjustment component 2506 can facilitatereceiving user input adjusting one or more parameters of the optimalAEC, resulting in a modified AEC pattern. Based on reception of the userinput, the inferencing component 318 can re-apply the one or more doseestimation models 304 and the one or more quality estimation models 308to the scout images to determine updated expected radiation doses to theone or more organs and updated measures of expected image quality in thetarget/background regions under the modified AEC pattern in real-time.The modified AEC can then be configured for the CT scanner via theacquisition control component 2508. In another example, the parameteradjustment component 2506 can facilitate receiving user input definingthe optimization criteria for the optimal AEC pattern. In particular,the parameter adjustment component 2506 can provide adjustment controlsthat allow the operating technician to provide input identifying atleast one of, a desired image quality for the target and/or backgroundregions and a desired radiation dose to the one or more organs. Based onreception of the user input, the optimization component 310 candetermine a modified AEC pattern that achieves the desired image qualityand the desired radiation dose. For example, in some implementations,the optimization component 310 can employ the optimal AEC evaluationcomponent 2002 to re-evaluate the optimal ACE using the modified ACEoptimization criteria in association with re-applying (e.g., by theinferencing component 318) the dose estimation model 304 and the qualityestimation model 308 to the scout images. Additionally, oralternatively, the one or more AEC optimization models 312 can include amodel configured to generate an optimal AEC pattern under user definedAEC optimization criteria and a selected task based on the scout images.Therefore, as the user selects the task or modifies other acquisitionparameters, the resulting impact on dose and IQ will dynamically be madeavailable, as well as an optimized AEC ready to scan the patient. Inaddition to configuring and controlling the CT scanner to perform the CTacquisition using the optimal AEC, information identifying the AEC used,the predicted dose and the predicted targe/background IQ can be includedin the scan report.

FIG. 26 presents a high-level flow diagram of another examplecomputer-implemented process for performing a CT exam using optimal AECin accordance with one or more embodiments of the disclosed subjectmatter. Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

In accordance with process 2600, at 2602, a system, operatively coupledto a processor (e.g., system 2500 and the like) receives (e.g., viareception component 314) task information identifying a task associatedwith performance of a CT scan of an anatomical region of a patient, andone or more scout images captured of the anatomical region. For example,in some implementations, the scout images and task information can bereceived in association with performance of an actual CT scan prior tothe capture of the high-resolution scan images (e.g., while the patientis on the scanning table). With these implementations, process 2600 canbe used to determine the optimal AEC for the patient and task for anactual imaging procedure in the clinical workflow and the scout imagescan be received directly from the CT scanner 108 (e.g., via theacquisition control component 2508).

At 2604, the system determines (e.g., using optimization component 310)an optimal AEC pattern for the CT scan using one or more deep learningnetworks (e.g., one or more optimization models 312, one or more doseestimation models 304 and/or one or more quality estimation models 308)that maximizes image quality in a target region of scan images to becaptured during the scan and minimizes radiation doses to organsincluded in the anatomical region, wherein the target region is based onthe task. At 2606, the system configures the CT scanner (e.g., CTscanner 108) to perform the CT scan using the optimal AEC pattern.

EXAMPLE OPERATING ENVIRONMENT

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, procedural programminglanguages, such as the “C” programming language or similar programminglanguages, and machine-learning programming languages such as like CUDA,Python, Tensorflow, PyTorch, and the like. The computer readable programinstructions can execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server using suitable processing hardware. In the latterscenario, the remote computer can be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection can be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In various embodiments involving machine-learning programminginstructions, the processing hardware can include one or more graphicsprocessing units (GPUs), central processing units (CPUs), and the like.For example, one or more of the disclosed machine-learning models (e.g.,the one or more dose estimation models 304, the one or more qualityestimation models 308, the one or more optimization models 312, the oneor more organ segmentation models 808, and the one or more anatomysegmentation models 1308) may be written in a suitable machine-learningprogramming language and executed via one or more GPUs, CPUs orcombinations thereof. In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It can be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In connection with FIG. 27 , the systems and processes described belowcan be embodied within hardware, such as a single integrated circuit(IC) chip, multiple ICs, an application specific integrated circuit(ASIC), or the like. Further, the order in which some or all of theprocess blocks appear in each process should not be deemed limiting.Rather, it should be understood that some of the process blocks can beexecuted in a variety of orders, not all of which can be explicitlyillustrated herein.

With reference to FIG. 27 , an example environment 2700 for implementingvarious aspects of the claimed subject matter includes a computer 2702.The computer 2702 includes a processing unit 2704, a system memory 2706,a codec 2735, and a system bus 2708. The system bus 2708 couples systemcomponents including, but not limited to, the system memory 2706 to theprocessing unit 2704. The processing unit 2704 can be any of variousavailable processors. Dual microprocessors, one or more GPUs, CPUs, andother multiprocessor architectures also can be employed as theprocessing unit 2704.

The system bus 2708 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 2706 includes volatile memory 2710 and non-volatilememory 2712, which can employ one or more of the disclosed memoryarchitectures, in various embodiments. The basic input/output system(BIOS), containing the basic routines to transfer information betweenelements within the computer 2702, such as during start-up, is stored innon-volatile memory 2712. In addition, according to present innovations,codec 2735 can include at least one of an encoder or decoder, whereinthe at least one of an encoder or decoder can consist of hardware,software, or a combination of hardware and software. Although, codec2735 is depicted as a separate component, codec 2735 can be containedwithin non-volatile memory 2712. By way of illustration, and notlimitation, non-volatile memory 2712 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), Flash memory, 3D Flashmemory, or resistive memory such as resistive random access memory(RRAM). Non-volatile memory 2712 can employ one or more of the disclosedmemory devices, in at least some embodiments. Moreover, non-volatilememory 2712 can be computer memory (e.g., physically integrated withcomputer 2702 or a mainboard thereof), or removable memory. Examples ofsuitable removable memory with which disclosed embodiments can beimplemented can include a secure digital (SD) card, a compact Flash (CF)card, a universal serial bus (USB) memory stick, or the like. Volatilememory 2710 includes random access memory (RAM), which acts as externalcache memory, and can also employ one or more disclosed memory devicesin various embodiments. By way of illustration and not limitation, RAMis available in many forms such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),and enhanced SDRAM (ESDRAM) and so forth.

Computer 2702 can also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 27 illustrates, forexample, disk storage 2714. Disk storage 2714 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD),flash memory card, or memory stick. In addition, disk storage 2714 caninclude storage medium separately or in combination with other storagemedium including, but not limited to, an optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage 2714 to thesystem bus 2708, a removable or non-removable interface is typicallyused, such as interface 2716. It is appreciated that disk storage 2714can store information related to a user. Such information might bestored at or provided to a server or to an application running on a userdevice. In one embodiment, the user can be notified (e.g., by way ofoutput device(s) 2736) of the types of information that are stored todisk storage 2714 or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected or shared with the server or application (e.g., byway of input from input device(s) 2728).

It is to be appreciated that FIG. 27 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 2700. Such software includes anoperating system 2718. Operating system 2718, which can be stored ondisk storage 2714, acts to control and allocate resources of thecomputer 2702. Applications 2720 take advantage of the management ofresources by operating system 2718 through program modules 2724, andprogram data 2726, such as the boot/shutdown transaction table and thelike, stored either in system memory 2706 or on disk storage 2714. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 2702 throughinput device(s) 2728. Input devices 2728 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 2704through the system bus 2708 via interface port(s) 2730. Interfaceport(s) 2730 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 2736 usesome of the same type of ports as input device(s) 2728. Thus, forexample, a USB port can be used to provide input to computer 2702 and tooutput information from computer 2702 to an output device 2736. Outputadapter 2734 is provided to illustrate that there are some outputdevices 2736 like monitors, speakers, and printers, among other outputdevices 2736, which require special adapters. The output adapters 2734include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 2736and the system bus 2708. It should be noted that other devices orsystems of devices provide both input and output capabilities such asremote computer(s) 2738.

Computer 2702 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)2738. The remote computer(s) 2738 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer2702. For purposes of brevity, only a memory storage device 2740 isillustrated with remote computer(s) 2738. Remote computer(s) 2738 islogically connected to computer 2702 through a network interface 2742and then connected via communication connection(s) 2744. Networkinterface 2742 encompasses wire or wireless communication networks suchas local-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 2744 refers to the hardware/softwareemployed to connect the network interface 2742 to the bus 2708. Whilecommunication connection 2744 is shown for illustrative clarity insidecomputer 2702, it can also be external to computer 2702. Thehardware/software necessary for connection to the network interface 2742includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationscan be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: a training component thattrains a first deep learning network to determine expected organ dosesabsorbed by one or more organs of an anatomical region under differentautomatic exposure control patterns of a computed tomography scan basedon scout images captured of the anatomical region for differentpatients, and a second deep learning network to determine expectedmeasures of image quality in target regions and background regions ofcomputed tomography scan images captured under the different automaticexposure control patterns based on the scout images; and an optimizationcomponent that employs the first deep learning network and the seconddeep learning network to determine optimal automatic exposure controlpatterns of the different automatic exposure control patterns forperformance of the computed tomography scan for the different patientsbased on the scout images.
 2. The system of claim 1, wherein theoptimization component determines the optimal automatic exposure controlpatterns using an objective function that selects the optimal automaticexposure control patterns based on maximizing image quality in thetarget regions and minimizing the expected organ doses.
 3. The system ofclaim 1, wherein the training component trains the first deep learningnetwork using a supervised machine learning process and ground truthdata comprising a plurality of computed tomography volumes generatedfrom computed tomography scans of the anatomical region under thedifferent automatic exposure control patterns and organ segmentationdose maps generated from the computed tomography volumes.
 4. The systemof claim 1, wherein the training component trains the second deeplearning network using a supervised machine learning process and groundtruth data image quality data generated using a plurality of computedtomography volumes generated from computed tomography scans of theanatomical region under the different automatic exposure controlpatterns, wherein the ground truth image quality data provides measuresof image quality in the target regions and the background regions asrepresented in the computed tomography volumes under the differentautomatic exposure control patterns.
 5. The system of claim 4, whereinthe computer executable components further comprise: a simulationcomponent that generates simulated noise projections using the computedtomography volumes and generates the ground truth image quality databased on the simulated noise projections.
 6. The system of claim 1,wherein the different automatic exposure control patterns reflectdifferent tube current modulations.
 7. The system of claim 1, whereinthe different automatic exposure control patterns reflect differentacquisition parameter values for one or more acquisition parametersselected from the group consisting of: tube voltage, collimation,filtration, bowtie, pitch, and start angle.
 8. The system of claim 1,wherein the target regions and the background regions vary for differenttasks and the optimal automatic exposure control patterns respectivelyrepresent different scout image and task combinations of the scoutimages and the different tasks, and wherein the training componentfurther trains a third deep learning network to determine the optimalautomatic exposure control patterns for each of the different scoutimage and task combinations.
 9. The system of claim 8, wherein thecomputer executable components further comprise: an inferencingcomponent that employs the third deep learning network to determine anoptimal automatic exposure pattern for a new computed tomography scan ofthe anatomical region of a patient based on one or more new scout imagescaptured of the anatomical region of the patient and a selected task ofthe different clinical tasks.
 10. The system of claim 9, wherein theinferencing component further employs the first deep learning network todetermine, based on the one or more new scout images, expected organdoses absorbed by the one or more organs of the patient under theoptimal automatic exposure pattern, and employs the second deep learningnetwork to determine an expected measure of image quality in the targetand background regions of scan images to be captured under the optimalautomatic exposure pattern.
 11. The system of claim 9, wherein thecomputer executable components further comprise: a reception componentthat receives the one or more new scout images from a computedtomography imaging device prior to performance of the new computedtomography scan using the imaging device; and a control componentoperatively coupled to the computed tomography imaging device thatcontrols performance of the new computed tomography scan by the computedtomography imaging device using the optimal automatic exposure pattern.12. The system of claim 11, wherein the computer executable componentsfurther comprise: an interface component that facilitates receiving userinput adjusting one or more parameters of the optimal exposure controlpattern, resulting in a modified automatic exposure control pattern, andwherein based on reception of the user input, the inferencing componentfurther employs the first deep learning network to determine, based onthe one or more new scout images, updated expected organ doses absorbedby the one or more organs of the patient under the updated automaticexposure pattern, and employs the second deep learning network todetermine an updated expected measure of image quality in the target andbackground regions of scan images to be captured under the updatedautomatic exposure pattern.
 13. The system of claim 9, wherein thecomputer executable components further comprise: an interface componentthat facilitates receiving user input identifying at least one of, adesired image quality for the target region and a desired radiation doseto the one or more organs, and wherein based on reception of the userinput, the inferencing component employs the third deep learning networkto determine, based on the one or more scout images, a modifiedautomatic exposure control pattern that achieves the desired imagequality and the desired radiation dose.
 14. A method, comprising:training, by a system operatively coupled to a processor, a first deeplearning network to determine expected organ doses absorbed by one ormore organs of an anatomical region under different automatic exposurecontrol patterns of a computed tomography scan based on scout imagescaptured of the anatomical region for different patients; training, bythe system, a second deep learning network to determine expectedmeasures of image quality in target regions and background regions ofcomputed tomography scan images captured under the different automaticexposure control patterns based on the scout images; and employing, bythe system, the first deep learning network and the second deep learningnetwork to determine optimal automatic exposure control patterns of thedifferent automatic exposure control patterns for performance of thecomputed tomography scan for the different patients based on the scoutimages.
 15. The method of claim 14, wherein the employing furthercomprises determining the optimal automatic exposure control patternsusing an objective function that selects the optimal automatic exposurecontrol patterns based on maximizing image quality in the target regionsand minimizing the expected organ doses.
 16. The method of claim 14,wherein the training the first deep learning network comprises employinga supervised machine learning process and ground truth data comprising aplurality of computed tomography volumes generated from computedtomography scans of the anatomical region under the different automaticexposure control patterns and organ segmentation dose maps generatedfrom the computed tomography volumes.
 17. The method of claim 14,wherein the training the first deep learning network comprises employinga supervised machine learning process and ground truth data imagequality data generated using a plurality of computed tomography volumesgenerated from computed tomography scans of the anatomical region underthe different automatic exposure control patterns, wherein the groundtruth image quality data provides measures of image quality in thetarget regions and the background regions as represented in the computedtomography volumes under the different automatic exposure controlpatterns.
 18. The method of claim 14, wherein the target regions and thebackground regions vary for different tasks and the optimal automaticexposure control patterns respectively represent different scout imageand task combinations of the scout images and the different tasks, andwherein the method further comprises: training, by the system, a thirddeep learning network to determine the optimal automatic exposurecontrol patterns for each of the different scout image and taskcombinations. employing, by the system, the third deep learning networkto determine an optimal automatic exposure pattern for a new computedtomography scan of the anatomical region of a patient based on one ormore new scout images captured of the anatomical region of the patientand a selected task of the different clinical tasks.
 19. Amachine-readable storage medium, comprising executable instructionsthat, when executed by a processor, facilitate performance ofoperations, comprising: training a first deep learning network todetermine expected organ doses absorbed by one or more organs of ananatomical region under different automatic exposure control patterns ofa computed tomography scan based on scout images captured of theanatomical region for different patients; training a second deeplearning network to determine expected measures of image quality intarget regions and background regions of computed tomography scan imagescaptured under the different automatic exposure control patterns basedon the scout images; and employing the first deep learning network andthe second deep learning network to determine optimal automatic exposurecontrol patterns of the different automatic exposure control patternsfor performance of the computed tomography scan for the differentpatients based on the scout images.
 20. The machine-readable storagemedium of claim 19, wherein the target regions and the backgroundregions vary for different tasks and the optimal automatic exposurecontrol patterns respectively represent different scout image and taskcombinations of the scout images and the different tasks, and whereinthe operations further comprise: training a third deep learning networkto determine the optimal automatic exposure control patterns for each ofthe different scout image and task combinations. employing the thirddeep learning network to determine an optimal automatic exposure patternfor a new computed tomography scan of the anatomical region of a patientbased on one or more new scout images captured of the anatomical regionof the patient and a selected task of the different clinical tasks.